The document summarizes research on the applications of artificial intelligence techniques in renewable energy systems, with a focus on solar power systems. It finds that artificial neural networks are the most commonly used AI technique, including backpropagation neural networks. Artificial neural networks provide advantages over other modeling techniques like short computing times and higher accuracy. The document concludes that artificial neural networks are well-suited for applications in solar energy prediction and optimization of solar power systems.
An effective identification of crop diseases using faster region based convol...IJECEIAES
The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
Gated recurrent unit decision model for device argumentation in ambient assis...IJECEIAES
The increasing elderly population worldwide is facing a variety of social, phys- ical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people pre- fer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among de- vices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
AI Driven Transformation: Advancing Clean Energy in Contemporary Power SystemsAJHSSR Journal
ABSTRACT: This paper presents a concise analysis of the critical role Artificial Intelligence (AI) plays in the
modernization and sustainability of power systems. It addresses the complex challenges arising from the
integration of renewable energy sources, distributed generators, and new technologies like electric vehicle
charging stations. AI emerges as a key solution, offering advanced data analysis and decision-making
capabilities to enhance efficiency and manage the increasing intricacy of power grids. The study synthesizes
insights from the International Energy Agency, notable case studies like Google's wind power forecasting, and
examples from industry leaders, applying a blend of quantitative and qualitative research methods. Through this
approach, it evaluates AI’s contributions to grid management, demand response, and operational efficiencies,
while also acknowledging the energy demands of AI systems themselves. Key findings highlight AI's potential
in optimizing real-time grid operations and improving consumer services, balanced against challenges such as
data privacy and the need for skilled personnel. The paper concludes with strategic recommendations for AI
adoption in the energy sector, emphasizing the importance of policy frameworks, international cooperation, and
ethical guidelines, as outlined in the EU's AI Act and OECD AI Principles. In essence, this study underlines
AI’s transformative role in driving power systems towards a future that is not only more efficient but also
sustainable and resilient, contingent upon a well-coordinated, regulated, and ethically informed approach.
Keywords –Artificial Intelligence (AI), Sustainable Energy, Power System Management, Renewable Energy
Integration, Data Analytics
Looking into the Crystal Ball: From Transistors to the Smart EarthThe Innovation Group
This paper is based on a keynote talk presented by Prof. Sangiovanni-Vincentelli at the 50th DAC. It discusses the evolution of cyber-physical and bio-cyber systems leading us to a smarter planet, and it predicts how EDA and embedded systems have to expand into this new field.
Indonesia is currently carrying out an industrial revolution 4.0. This
revolution discusses the application of technology in the industrial sector,
one of which is the agricultural sector. In addition to discussing the
application of technology, this revolution also supports the use of renewable
energy sources and one of them is the application of solar energy. The
application of technology in the agricultural sector is expected to help
farmers in maintaining crops to reduce the possibility of crop failure. The
existence of this statement makes researchers conduct research in the design
and construction of systems with internet of things (IoT) technology and
utilize solar energy sources as energy sources for the system. The IoT
system will utilize the ATmega328P+ESP8266 RobotDyn microcontroller
by utilizing the DHT22, MD0127, soil moisture sensor, and BH1750FVI
sensors and sending data to Thingspeak by utilizing the internet network
with HTTP communication protocols. The system can monitor ecological
factors in gardens with a fairly good degree of accuracy and the utilization of
solar energy can run the system properly.
An approach based on deep learning that recommends fertilizers and pesticide...IJECEIAES
With the advancement of the internet, individuals are becoming more reliant on online applications to meet most of their needs. In the meantime, they have very little spare time to devote to the selection and decision-making process. As a result, the need for recommender systems to help tackle this problem is expanding. Recommender systems successfully provide consumers with individualized recommendations on a variety of goods, simplifying their duties. The goal of this research is to create a recommender system for farmers based on tree data structures. Recommender system has become interesting research by simplifying and saving time in the decision-making process of users. We conducted although a lot of research in various fields, there are insufficient in the agriculture sector. This issue is more necessary for farmers in Quangnam-Danang or all Vietnam countries by severe climate features. Storm from that, this research designs a system based on tree data structures. The proposed model combines the you only look once (YOLO) algorithm in a convolutional neural network (CNN) model with a similarity tree in computing similarity. By experiments on 400 samples and evaluating precision, accuracy, and the value of the predictive test as determined by its positive predictive value (PPV), the research proves that the proposed model is feasible and gain better results compared with other state-of-the-art models.
An effective identification of crop diseases using faster region based convol...IJECEIAES
The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
Gated recurrent unit decision model for device argumentation in ambient assis...IJECEIAES
The increasing elderly population worldwide is facing a variety of social, phys- ical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people pre- fer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among de- vices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
AI Driven Transformation: Advancing Clean Energy in Contemporary Power SystemsAJHSSR Journal
ABSTRACT: This paper presents a concise analysis of the critical role Artificial Intelligence (AI) plays in the
modernization and sustainability of power systems. It addresses the complex challenges arising from the
integration of renewable energy sources, distributed generators, and new technologies like electric vehicle
charging stations. AI emerges as a key solution, offering advanced data analysis and decision-making
capabilities to enhance efficiency and manage the increasing intricacy of power grids. The study synthesizes
insights from the International Energy Agency, notable case studies like Google's wind power forecasting, and
examples from industry leaders, applying a blend of quantitative and qualitative research methods. Through this
approach, it evaluates AI’s contributions to grid management, demand response, and operational efficiencies,
while also acknowledging the energy demands of AI systems themselves. Key findings highlight AI's potential
in optimizing real-time grid operations and improving consumer services, balanced against challenges such as
data privacy and the need for skilled personnel. The paper concludes with strategic recommendations for AI
adoption in the energy sector, emphasizing the importance of policy frameworks, international cooperation, and
ethical guidelines, as outlined in the EU's AI Act and OECD AI Principles. In essence, this study underlines
AI’s transformative role in driving power systems towards a future that is not only more efficient but also
sustainable and resilient, contingent upon a well-coordinated, regulated, and ethically informed approach.
Keywords –Artificial Intelligence (AI), Sustainable Energy, Power System Management, Renewable Energy
Integration, Data Analytics
Looking into the Crystal Ball: From Transistors to the Smart EarthThe Innovation Group
This paper is based on a keynote talk presented by Prof. Sangiovanni-Vincentelli at the 50th DAC. It discusses the evolution of cyber-physical and bio-cyber systems leading us to a smarter planet, and it predicts how EDA and embedded systems have to expand into this new field.
Indonesia is currently carrying out an industrial revolution 4.0. This
revolution discusses the application of technology in the industrial sector,
one of which is the agricultural sector. In addition to discussing the
application of technology, this revolution also supports the use of renewable
energy sources and one of them is the application of solar energy. The
application of technology in the agricultural sector is expected to help
farmers in maintaining crops to reduce the possibility of crop failure. The
existence of this statement makes researchers conduct research in the design
and construction of systems with internet of things (IoT) technology and
utilize solar energy sources as energy sources for the system. The IoT
system will utilize the ATmega328P+ESP8266 RobotDyn microcontroller
by utilizing the DHT22, MD0127, soil moisture sensor, and BH1750FVI
sensors and sending data to Thingspeak by utilizing the internet network
with HTTP communication protocols. The system can monitor ecological
factors in gardens with a fairly good degree of accuracy and the utilization of
solar energy can run the system properly.
An approach based on deep learning that recommends fertilizers and pesticide...IJECEIAES
With the advancement of the internet, individuals are becoming more reliant on online applications to meet most of their needs. In the meantime, they have very little spare time to devote to the selection and decision-making process. As a result, the need for recommender systems to help tackle this problem is expanding. Recommender systems successfully provide consumers with individualized recommendations on a variety of goods, simplifying their duties. The goal of this research is to create a recommender system for farmers based on tree data structures. Recommender system has become interesting research by simplifying and saving time in the decision-making process of users. We conducted although a lot of research in various fields, there are insufficient in the agriculture sector. This issue is more necessary for farmers in Quangnam-Danang or all Vietnam countries by severe climate features. Storm from that, this research designs a system based on tree data structures. The proposed model combines the you only look once (YOLO) algorithm in a convolutional neural network (CNN) model with a similarity tree in computing similarity. By experiments on 400 samples and evaluating precision, accuracy, and the value of the predictive test as determined by its positive predictive value (PPV), the research proves that the proposed model is feasible and gain better results compared with other state-of-the-art models.
Usability Engineering, Human Computer Interaction and Allied Sciences: With R...Scientific Review SR
Human Computer Interaction is actually responsible for the designing of the computing technologies keeping in mind the aspects of Interaction. Some of the fields viz. Man-Machine Interaction (MMI), User Experience Designing, User Experience Design, Human Centered Designing etc and importantly all these systems and technologies are dedicated to the designing of interface of various tools and systems such as computers, laptops, electronic systems, smart phones etc. Information Technology field is growing rapidly and there are various technologies are increasing viz. Big Data Management, Cloud Computing, Green Computing, Data Science, Internet of Things (IoT), HCI, Usability Engineering etc. Usability Engineering is gaining as a field of study as well and dedicated in creation of the higher usability and user friendliness of the electronic tools and products. In this field few aspects and technologies are most important and emerging viz. Human cognition, behavioral Research Methods, Quantitative techniques etc for the development of usability systems. Designing, implementation, usability even in multimedia material viz. audio-video may also practice in the Usability Engineering and allied fields. Wireframes including few other prototypes are required in maintaining of the better and healthy man and machine interaction. As the field is growing therefore, it is applicable in other sectors and allied areas and among these agriculture is important one. In agricultural sector different applications of information technologies are increasing and among this Usability Engineering and HCI are important one. In pre production and also in post production; directly and indirectly this technology is emerging and growing. This paper talks about the basics of this technologies and also its current and future technologies with reference to academic potentialities of this branch in Agricultural Informatics programs.
Sustainable computing is a new pathway in the research field. because it is clear the growth of ICT industries globally is rapidly poisoning our environment. So ultimately we need to give attention to this for more Sustainable computing solutions.
The smart grid is an electrical power grid that is integrated with an AI enabled, two way communication network providing energy and information. It is a technology that enables instantaneous feedback from various sensors and devices on the operation of the power grid. Although AI is relatively new, it is poised to revolutionize the way we produce, transmit, and consume energy. AI will constitute the brain of future smart grid. The power sector has started to use AI and related technologies for communication between smart grids, smart meters, and Internet of things devices. This paper presents some applications of AI in smart grid. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Artificial Intelligence in Smart Grid" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50563.pdf Paper URL: https://www.ijtsrd.com/engineering/other/50563/artificial-intelligence-in-smart-grid/matthew-n-o-sadiku
Electronics in medical sciences has been an emerging field of study and has evolved a lot. Bio electronics is a somewhat new branch that can provide more effective and convenient solutions by revolutionizing the scope of medicine forever. It involves electronic devices that can be consumed furthermore after going inside the body, capable of assisting in various procedures like a diagnosis, surgical assistance, etc. This paper focusses on delivering the fundamental concept of edible electronics, how is it helpful, its extent of application, and its challenges. Anshika Gupta "Bioelectronics - The Revolutionary Concept" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33022.pdf Paper Url :https://www.ijtsrd.com/biological-science/other/33022/bioelectronics--the-revolutionary-concept/anshika-gupta
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENTijasa
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine
communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment.The underlying idea is that such systems must have self-x properties in order to adapt their behavior to
external disturbances with a high-degree of autonomy.
The integration of information technology with renewable energy entails leveraging digital innovations to optimize the efficiency, reliability, and effectiveness of renewable energy systems. By harnessing technologies such as data analytics, IoT, and AI, this synergy enables smarter energy management, enhances grid stability, and fosters the transition to a cleaner, more sustainable energy future.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Data Science for Building Energy Management a reviewMigue.docxrandyburney60861
Data Science for Building Energy Management: a review
Miguel Molina-Solanaa,b, Maŕıa Rosa,∗, M. Dolores Ruiza, Juan Gómez-Romeroa, M.J. Martin-Bautistaa
aDepartment of Computer Science and Artificial Intelligence, Universidad de Granada
bData Science Institute, Imperial College London
Abstract
The energy consumption of residential and commercial buildings has risen steadily in recent years, an
increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well
as user behavior influence the quantity and quality of the energy consumed daily in buildings. However,
technology is now available that can accurately monitor, collect, and store the huge amount of data involved
in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful
ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting
a great deal of attention and interest. This paper reviews how Data Science has been applied to address the
most difficult problems faced by practitioners in the field of Energy Management, especially in the building
sector. The work also discusses the challenges and opportunities that will arise with the advent of fully
connected devices and new computational technologies.
1. Introduction
There is a general consensus in the world today that human activities are having a negative impact
on the environment and have accelerated both global warming and climate change. These environmental
threats have been intensified by the emissions produced by the energy required for the lighting and HVAC
(heating, ventilation and air-conditioning) systems in building constructions. According to the International
Energy Agency (IEA), residential and commercial buildings are responsible for up to 32% of the total final
energy consumption. In fact, in most IEA countries, they account for approximately 40% of the primary
energy consumption. Similar statistics are given by the World Business Council for Sustainable Development
(WBCSD) within the framework of its Energy Efficiency in Buildings (EEB) project1. Also provided is a
comprehensive review [1] of the state of the art in building energy use (with a primary focus on energy
demand).
These data indicate that inefficient energy management in aging buildings combined with rising construc-
tion activity in developed countries will cause energy consumption to soar in the near future and heighten the
negative impacts associated with this consumption. Moreover, variable energy costs call for the implemen-
tation of more intelligent strategies to adapt and reduce energy consumption as well as to find alternative
and sustainable energy sources. The relevance of these issues is clearly reflected in the research priorities of
the European Union, as stated in its Horizon2020 Societal Challenge “Secure, Clean and Efficient Energy”.
This work program targets a significant reduction in energy consu.
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
A new smart approach of an efficient energy consumption management by using a...IJEECSIAES
Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (Ap) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (Ap) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
A new smart approach of an efficient energy consumption management by using a...nooriasukmaningtyas
Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
El artículo presenta los resultados obtenidos del cálculo: potencia óptima de generación, conectada en un punto requerido de la red, que minimice las pérdidas del sistema de distribución. Para la búsqueda de dicha potencia se hizo uso del algoritmo de optimización por enjambre de partículas o PSO (por sus siglas en inglés), en el entorno del lenguaje de programación de DIgSILENT (DPL).
Los resultados mostraron que el algoritmo resultó muy eficiente en la codificación, así como se consiguió una rápida convergencia. Ello, hace posible su aplicación en redes de distribución, balanceadas y desbalanceadas.
Usability Engineering, Human Computer Interaction and Allied Sciences: With R...Scientific Review SR
Human Computer Interaction is actually responsible for the designing of the computing technologies keeping in mind the aspects of Interaction. Some of the fields viz. Man-Machine Interaction (MMI), User Experience Designing, User Experience Design, Human Centered Designing etc and importantly all these systems and technologies are dedicated to the designing of interface of various tools and systems such as computers, laptops, electronic systems, smart phones etc. Information Technology field is growing rapidly and there are various technologies are increasing viz. Big Data Management, Cloud Computing, Green Computing, Data Science, Internet of Things (IoT), HCI, Usability Engineering etc. Usability Engineering is gaining as a field of study as well and dedicated in creation of the higher usability and user friendliness of the electronic tools and products. In this field few aspects and technologies are most important and emerging viz. Human cognition, behavioral Research Methods, Quantitative techniques etc for the development of usability systems. Designing, implementation, usability even in multimedia material viz. audio-video may also practice in the Usability Engineering and allied fields. Wireframes including few other prototypes are required in maintaining of the better and healthy man and machine interaction. As the field is growing therefore, it is applicable in other sectors and allied areas and among these agriculture is important one. In agricultural sector different applications of information technologies are increasing and among this Usability Engineering and HCI are important one. In pre production and also in post production; directly and indirectly this technology is emerging and growing. This paper talks about the basics of this technologies and also its current and future technologies with reference to academic potentialities of this branch in Agricultural Informatics programs.
Sustainable computing is a new pathway in the research field. because it is clear the growth of ICT industries globally is rapidly poisoning our environment. So ultimately we need to give attention to this for more Sustainable computing solutions.
The smart grid is an electrical power grid that is integrated with an AI enabled, two way communication network providing energy and information. It is a technology that enables instantaneous feedback from various sensors and devices on the operation of the power grid. Although AI is relatively new, it is poised to revolutionize the way we produce, transmit, and consume energy. AI will constitute the brain of future smart grid. The power sector has started to use AI and related technologies for communication between smart grids, smart meters, and Internet of things devices. This paper presents some applications of AI in smart grid. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Artificial Intelligence in Smart Grid" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50563.pdf Paper URL: https://www.ijtsrd.com/engineering/other/50563/artificial-intelligence-in-smart-grid/matthew-n-o-sadiku
Electronics in medical sciences has been an emerging field of study and has evolved a lot. Bio electronics is a somewhat new branch that can provide more effective and convenient solutions by revolutionizing the scope of medicine forever. It involves electronic devices that can be consumed furthermore after going inside the body, capable of assisting in various procedures like a diagnosis, surgical assistance, etc. This paper focusses on delivering the fundamental concept of edible electronics, how is it helpful, its extent of application, and its challenges. Anshika Gupta "Bioelectronics - The Revolutionary Concept" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33022.pdf Paper Url :https://www.ijtsrd.com/biological-science/other/33022/bioelectronics--the-revolutionary-concept/anshika-gupta
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENTijasa
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine
communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment.The underlying idea is that such systems must have self-x properties in order to adapt their behavior to
external disturbances with a high-degree of autonomy.
The integration of information technology with renewable energy entails leveraging digital innovations to optimize the efficiency, reliability, and effectiveness of renewable energy systems. By harnessing technologies such as data analytics, IoT, and AI, this synergy enables smarter energy management, enhances grid stability, and fosters the transition to a cleaner, more sustainable energy future.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Data Science for Building Energy Management a reviewMigue.docxrandyburney60861
Data Science for Building Energy Management: a review
Miguel Molina-Solanaa,b, Maŕıa Rosa,∗, M. Dolores Ruiza, Juan Gómez-Romeroa, M.J. Martin-Bautistaa
aDepartment of Computer Science and Artificial Intelligence, Universidad de Granada
bData Science Institute, Imperial College London
Abstract
The energy consumption of residential and commercial buildings has risen steadily in recent years, an
increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well
as user behavior influence the quantity and quality of the energy consumed daily in buildings. However,
technology is now available that can accurately monitor, collect, and store the huge amount of data involved
in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful
ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting
a great deal of attention and interest. This paper reviews how Data Science has been applied to address the
most difficult problems faced by practitioners in the field of Energy Management, especially in the building
sector. The work also discusses the challenges and opportunities that will arise with the advent of fully
connected devices and new computational technologies.
1. Introduction
There is a general consensus in the world today that human activities are having a negative impact
on the environment and have accelerated both global warming and climate change. These environmental
threats have been intensified by the emissions produced by the energy required for the lighting and HVAC
(heating, ventilation and air-conditioning) systems in building constructions. According to the International
Energy Agency (IEA), residential and commercial buildings are responsible for up to 32% of the total final
energy consumption. In fact, in most IEA countries, they account for approximately 40% of the primary
energy consumption. Similar statistics are given by the World Business Council for Sustainable Development
(WBCSD) within the framework of its Energy Efficiency in Buildings (EEB) project1. Also provided is a
comprehensive review [1] of the state of the art in building energy use (with a primary focus on energy
demand).
These data indicate that inefficient energy management in aging buildings combined with rising construc-
tion activity in developed countries will cause energy consumption to soar in the near future and heighten the
negative impacts associated with this consumption. Moreover, variable energy costs call for the implemen-
tation of more intelligent strategies to adapt and reduce energy consumption as well as to find alternative
and sustainable energy sources. The relevance of these issues is clearly reflected in the research priorities of
the European Union, as stated in its Horizon2020 Societal Challenge “Secure, Clean and Efficient Energy”.
This work program targets a significant reduction in energy consu.
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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (Ap) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (Ap) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
El artículo presenta los resultados obtenidos del cálculo: potencia óptima de generación, conectada en un punto requerido de la red, que minimice las pérdidas del sistema de distribución. Para la búsqueda de dicha potencia se hizo uso del algoritmo de optimización por enjambre de partículas o PSO (por sus siglas en inglés), en el entorno del lenguaje de programación de DIgSILENT (DPL).
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
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Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
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2. and systems that perform operations similar to human
learning and decision-making [36]. It can also be defined as
a system's ability to understand external information
properly, learn from that information, and practice those
learnings to accomplish definite goals and tasks over flexible
adaptation [37].
AI can be simply described as providing intelligence to
machines that can process information and makes decisions,
just the way human beings think of all information received
and act on that information when needed. It is used to make
machines or entities intelligent to perform tasks efficiently for
complex problems [38-39]. Still, it has yet to achieve the level
of intelligence of the human brain fully. With this kind of
description, robots always come to mind. Machines can only
act similar to human beings, if and only if, devices have rich
data relating to their environment. Machines needed to have
proper and a large chunk of information to make sense of
what is happening around their environment to solve complex
problems.
For AI to simply work and provide value, it needs IoTs
comprising of sensors and other data-gathering tools to
collect the required information from the environment it
needed. Those collected data are stored with proper
configurations and are analyzed. With systems capable of
collecting tens or hundreds of millions or massive volumes of
data gathered from the environment and stored in the
database, the process of storing such information and the
analysis becomes more complicated. The data collected are
commonly analog formats often gathered from the
environment and included texts or numbers. Environmental
information that provides for temperature and humidity can
be another form of data.
Artificial intelligence introduces techniques and
methodologies for a machine, device, or a robot to make
sense of the information collected and processed. Machines
are configured to self-learn with the information available
and act on the corresponding inputs from the environment.
This artificial intelligence in the apparatus, devices, or
systems leverages on the IoTs, big data, and AI.
For a device, system, or a machine to fully imitate all
aspects of the human being, they need to have all four
fundamental characteristics.
AIs are also classified as analytical, human-inspired, or
humanized AIs [37]. The following are their definitions:
• Analytical AIs - producing a cognitive representation of
the environment and learn based on the historical
understandings or events to apprise forthcoming
choices;
• human-inspired AIs – comprehend human feelings and
study them in making choices for humanized AI;
• humanized AIs - these systems are designed to be self-
conscious in their exchanges with others.
One specific AI application in university is the AI-based
virtual teaching assistant (VTA) implemented in Georgia
Tech to answer students' queries in their virtual library. Jill
Watson is the name of the virtual assistant of the university
[37].
B. AI Applications and Techniques
AI improves how people work today, especially on the
improvement or shift from manual computations. In several
studies, AI has proven its use in the field of medicine or
healthcare and even in accounting databases and computer
games [40-45].
AI is extensively used in agriculture, specifically on the
applications in rice diseases, crop management, pest
management, product monitoring and control, soil and
irrigation management, weed management, and yield
prediction, among others [46-48]. In medicine and healthcare,
diagnostic imaging is considered as top area where AI is
extensively used in research, followed by genetic and
electrodiagnosis.
In medicine, AI has done wonders to understand better
diseases that include the use of AI tools in cancer, disorders in
the brain (stroke), and the heart. AI tools are often used to
detect and diagnose early warning signs of diseases, treatment,
and prediction of an outcome and prognosis evaluation [49-
50]. Several studies have also shown that AI can be used in
biometric and forensics [51-53]. There seem to be endless
possibilities of the use and applications of AI.
1. AI Tools and Techniques
AI was founded on different learning theories. That
includes evolutionary learning, statistical learning, and neural
learning [54-55]. Neural Network (NN) and Support Vector
Machines (SVM) are the two most common learning
algorithms used in many works of literature [56]. Other
algorithms include Linear Regression, Nearest Neighbor,
Naïve Bayes, Logistic Regressions, Random Forests,
Decision Tree, and Hidden Markov [56].
2. Neural Network
Several studies were conducted in Neural Network,
specifically in the Artificial Neural Network or ANN [57-61].
The ANN was founded in 1943 by McCulloch and Pitts. ANN
is considered the most ultimate AI technique about the
mathematical model for the human brain's (neuron) primitive
cell [62-63]. The mind is activated when the input's weighted
total surpasses the desired value wherein the output responds
to the activated functions.
The ANN can fine-tune the figures to correct the output's
fault, making that a very controlling tool for machine learning
[56]. Fig. 5 shows a simple illustration of a neural network and
the relationship between the input and output and the outcome.
3. Support Vector Machines
The SVMs are typically used to classify objects in two
groups, which are managed to learn models with associated
learning procedures. The data are analyzed and used for
classification and regression analysis. SVMs can also
proficiently accomplish a non-linear classification using a
technique or process called kernel trick wherein indirectly
charting their inputs into feature spaces with high dimensions
[63-64].
The classification tasks are not considered simple and are
always regarded as complicated, and with that, complex
structures are required for the optimum object separation [65].
3. C. AI in Renewable Energy Systems
Renewable energy systems are becoming advanced by
introducing smarter systems such as IoT and AI being
integrated to make these RES robust and more responsive. AI
technologies are now being used in all RE systems from wind,
solar, hydro, ocean, geothermal to solar photovoltaic systems,
among others [62]. It has shown that with the use of AI in RE
systems, it made the technologies like the panels, solar PV,
and wind turbines systems becoming more cost-effective and
efficient, thus become more ubiquitous and with the potential
outcome of removing the fossil fuel-based power plants [66].
The following are the different AI techniques being used
in renewable energy systems: support vector machines
(SVM), Feed-forward backpropagation neural network
(BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS),
Naïve Bayes (NB), Genetic Algorithm (GA), Artificial Neural
Network (ANN), Radial basis function neural networks
(RBFNN), and seasonal autoregressive integrated moving
average (SARIMA), HIstorical SImilar Mining (HISIMI),
Support Vector Machines (SVM), Transient System
Simulation Tool (TRNSYS), Group method of data handling
neural network (GMDHNN), Radial basis function (RBF),
autoregressive integrated moving average (ARIMA). ANN
stood out as the best of these techniques [67-71].
Among these techniques came a more versatile one, the
ANN. ANN's specific advantages over its competition include
short computing time, higher accuracy, and generalization
capabilities over other modeling techniques. This would
translate to cost efficiency over other modeling techniques
[72]. The same study covers many applications of ANN
techniques in various fields that include medicine, science,
engineering, environmental, agriculture, mining, technology,
climate, business, arts, and nanotechnology, among others.
The study assesses ANN contributions, compare
performances, and critiques methods. Neural-network models
such as feedforward and feedback propagation artificial neural
networks perform better in applying to human problems [72].
The same study determined that the feedforward and feedback
propagation ANN models are the best fit for data analysis
factors like accuracy, processing speed, latency, fault
tolerance, volume, scalability, convergence, and performance.
1. AI Applications in Solar Energy
The significance of the AI applications in solar power
systems is detailed in different review articles [74-76].
The AI-based studies of Mellita et al. focus on the design
and sizing of the solar photovoltaic (PV), which is considered
one of the most important considerations. As mentioned, AI
methodologies are used in many applications. One such
widespread use is solar radiation modeling, prediction, and
forecasting. Among the many techniques introduced in solar
energy literature, the Artificial Neural Network (ANN) was
the most often used method according to the studies published
[77-102].
It has been determined from these studies that there are
correlations that exist with 98-99% and 94-96% between the
actual and the predicted solar irradiance during the sunny days
and cloudy days, respectively [103].
TABLE I. THE SUMMARY OF AI TECHNIQUES OR METHODS USED IN
DIFFERENT APPLICATIONS FROM AMONG THE 40 SOLAR ENERGY RESEARCH
PUBLICATIONS
Methods Used Applications
Backpropagation
neural network
(BPNN)
Solar irradiance prediction, solar radiation
prediction, water heating system, beam
radiation prediction, daily ambient
temperature prediction, daily solar
irradiation prediction, maximum power of
HCPV prediction, global solar irradiation
prediction,
Solar energy and hot water quantity
prediction, Solar energy prediction, and
building energy prediction.
ANFIS PV power supply modeling, Hourly global
irradiance prediction, Clearness index,
radiation prediction, PV power supply
modeling, Solar power prediction, and SCPP
performance prediction
GA Solar tracking, Design of solar water heating system
BPNN and Batch
Learning ANN
Prediction of mean temperature
BNN and Angstrom
linear methods
Prediction of global solar radiation
BNN and Regression
Methods
Prediction of global solar radiation
GA+HISIMI Prediction of solar power
RBF+IIR and
BPNN+IIR
Optimization of PV systems
ANN+TRNSYS Prediction of performance of ICS
WT+BPNN Values estimation of solar radiation
PPF, Support Vector
Machines Cloudy
Prediction of solar power
BPNN + GA Prediction of solar power
GA, PO MPPT of PV array
GA+GMDHNN Optimization of solar power systems
RBFNN+WT Prediction of PV energy
SVR, BPNN PV energy prediction
Support Vector
Machines, RBFNN,
Autoregressive
Prediction of solar power
Many researchers have been using BPNN to predict solar
irradiance, solar radiation, solar energy, water heating system,
beam radiation, daily ambient temperature, daily solar
irradiation, and a maximum power of HCPV prediction.
Researchers also used BPNN for global solar irradiation
prediction, solar energy and hot water quantity prediction,
Solar energy prediction, and building energy prediction.
ANFIS was used in PV power supply modeling, Hourly global
irradiance prediction, clearness index, radiation prediction,
PV power supply modeling, solar power prediction, and SCPP
performance prediction. There were instances wherein a
combination of different techniques is applied to obtain
optimum results in predictions, estimations, and forecasting.
The root-mean-square error (RMSE) is often used to
determine the differences between the sample or population
values predicted by a given model and the observed amounts.
It is used as a typical statistical metric to quantify model
performance, such as in meteorology or air quality [104].
2. AI Applications in Solar Microgrid
AI applications are considered significant as well in
the solar microgrid applications. A summary of the findings
can be found in Table II.
4. TABLE II. THE SUMMARY OF AI TECHNIQUES OR METHODS USED IN A
SOLAR MICROGRID.
Author &
Year of
Publica-tion
AI
Techniques
Involved
Application Findings
[104] Bacterial
foraging
optimization
(BFO);
Particle
swarm
optimization
(PSO);
GA;
Tuning of the
significant
parameters’
in automatic
generation
control in
microgrid.
BFO is superior
to PSO and GA
in the
simulations
conducted with
different
parameters in
the microgrid.
[105] ARIMA time
series
algorithm
using
continuous
data.
Electric
consumption
prediction in
a solar PV
microgrid
village.
There was a
correlation on the
predicted and
actual values.
[106] Machine
learning
algorithm
using
regression
tree model
Solar
microgrid
power
generation
output
prediction
Predicted versus
actual results
obtained 85%
and 77%
accuracy (AM
and PM
respectively.
[107] Artificial
neural
network
(ANN) was
used for the
prediction
model.
Solar energy
output
prediction
Predicted versus
actual output
was at 0.5–9%
difference.
[108] Sugeno type-
fuzzy system;
Q-learning
algorithm
Energy
management
Indicated a good
performance of
the Sugeno type-
fuzzy system.
IV. CONCLUSION
In this paper, the AI applications were presented in
different industry segments, such as medicine, agriculture,
education, corporations, and the government, specifically in
the introductory part. AI applications and techniques in RES
were discussed coming from various journal articles.
Different authors used several AI methodologies. In solar
energy, the most popular methods used were ANN BPNN,
ANFIS, and GA. These techniques are widely used in
different types of solar predictions based on the findings of
this review. ANN stood out as the best of these techniques.
ANN's specific advantages over its competition include short
computing time, higher accuracy, and generalization
capabilities over other modeling techniques. This would
translate to cost efficiency over other modeling techniques.
Future work in AI is a combination of other approaches to
further optimize prediction models' accuracy, especially in
solar radiation predictions.
V. REFERENCES
[1] Marih, S., Ghomri, L., & Bekkouche, B. (2020). Evaluation of the
Wind Potential and Optimal Design of a Wind Farm in The Arzew
Industrial Zone in Western Algeria. International Journal of Renewable
Energy Development, 9(2), 177-187.
[2] Ahmed, O. K., Daoud, R. W., Bawa, S. M., & Ahmed, A. H. (2020).
Optimization of PV/T Solar Water Collector based on Fuzzy Logic
Control. International Journal of Renewable Energy Development,
9(2).
[3] Maleki, A., Nazari, M. A., & Pourfayaz, F. (2020). Harmony search
optimization for optimum sizing of hybrid solar schemes based on a
battery storage unit. Energy Reports.
[4] Ariae, A. R., Jahangiri, M., Fakhr, M. H., & Shamsabadi, A. A. (2019).
Simulation of Biogas Utilization Effect on The Economic Efficiency
and Greenhouse Gas Emission: A Case Study in Isfahan, Iran.
International Journal of Renewable Energy Development, 8(2).
[5] Chatterjee, A., Brent, A., Rayudu, R., & Verma, P. (2019). Microgrids
For Rural Schools: An Energy-Education Accord to Curb Societal
Challenges for Sustainable Rural Developments. International Journal
of Renewable Energy Development, 8(3).
[6] Dellosa, Jeffrey T. "Potential Effect and Analysis of High Residential
Solar Photovoltaic (PV) Systems Penetration to an Electric
Distribution Utility (DU)." International Journal of Renewable Energy
Development 5.3 (2016).
[7] Cai, W., Li, X., Maleki, A., Pourfayaz, F., Rosen, M. A., Nazari, M.
A., & Bui, D. T. (2020). Optimal sizing and location based on
economic parameters for an off-grid application of a hybrid system
with photovoltaic, battery, and diesel technology. Energy, 117480.
[8] Kannan, N., & Vakeesan, D. (2016). Solar energy for the future world:
-A review. Renewable and Sustainable Energy Reviews, 62, 1092-
1105.
[9] Vandaele, N., & Porter, W. (2015). Renewable energy in developing
and developed nations: Outlooks to 2040. Journal of Undergraduate
Research, 15(3), 1-7.
[10] Khan, J., & Arsalan, M. H. (2016). Solar power technologies for
sustainable electricity generation–A review. Renewable and
Sustainable Energy Reviews, 55, 414-425.
[11] Roche, O. M., & Blanchard, R. E. (2018). Design of a solar energy
center for providing lighting and income-generating activities for off-
grid rural communities in Kenya. Renewable Energy, 118, 685-694.
[12] Barman, M., Mahapatra, S., Palit, D., & Chaudhury, M. K. (2017).
Performance and impact evaluation of solar home lighting systems on
the rural livelihood in Assam, India. Energy for Sustainable
Development, 38, 10-20.
[13] Graber, S., Narayanan, T., Alfaro, J., & Palit, D. (2018). Solar
microgrids in rural India: Consumers' willingness to pay for attributes
of electricity. Energy for Sustainable Development, 42, 32-43.
[14] Urpelainen, J., & Yoon, S. (2015). Solar home systems for rural India:
Survey evidence on awareness and willingness to pay from Uttar
Pradesh. Energy for sustainable development, 24, 70-78.
[15] Urpelainen, J., & Yoon, S. (2015). Solar home systems for rural India:
Survey evidence on awareness and willingness to pay from Uttar
Pradesh. Energy for sustainable development, 24, 70-78.
[16] Sharma, H., Haque, A., & Jaffery, Z. A. (2018). Solar energy
harvesting wireless sensor network nodes: A survey. Journal of
Renewable and Sustainable Energy, 10(2), 023704.
[17] Kekre, A., & Gawre, S. K. (2017, October). Solar photovoltaic remote
monitoring system using IoT. In 2017 International Conference on
Recent Innovations in Signal Processing and Embedded Systems
(RISE) (pp. 619-623). IEEE.
[18] Zhang, X., Bao, J., Wang, R., Zheng, C., & Skyllas-Kazacos, M.
(2017). Dissipativity based distributed economic model predictive
control for residential microgrids with renewable energy generation
and battery energy storage. Renewable Energy, 100, 18-34.
[19] Gandini, D., & de Almeida, A. T. (2017). Direct current microgrids
based on solar power systems and storage optimization are a tool for
cost-effective rural electrification. Renewable energy, 111, 275-283.
[20] Singh, A., Baredar, P., & Gupta, B. (2017). Techno-economic
feasibility analysis of hydrogen fuel cell and solar photovoltaic hybrid
renewable energy system for academic research building. Energy
Conversion and Management, 145, 398-414.
[21] Morano, P., Tajani, F., & Locurcio, M. (2017). GIS application and
econometric analysis to verify the financial feasibility of roof-top wind
turbines in the city of Bari (Italy). Renewable and Sustainable Energy
Reviews, 70, 999-1010.
[22] Djørup, S., Thellufsen, J. Z., & Sorknæs, P. (2018). The electricity
market in a renewable energy system. Energy, 162, 148-157.
[23] Singh, A., Baredar, P., & Gupta, B. (2017). Techno-economic
feasibility analysis of hydrogen fuel cell and solar photovoltaic hybrid
renewable energy system for academic research building. Energy
Conversion and Management, 145, 398-414.
[24] Safarzyńska, K., & van den Bergh, J. C. (2017). Financial stability at
risk due to investing rapidly in renewable energy. Energy Policy, 108,
12-20.
[25] Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017).
Renewable energy: Present research and future scope of Artificial
5. Intelligence. Renewable and Sustainable Energy Reviews, 77, 297-
317.
[26] Komeilibirjandi, A., Raffiee, A. H., Maleki, A., Nazari, M. A., &
Shadloo, M. S. (2020). Thermal conductivity prediction of nanofluids
containing CuO nanoparticles by using correlation and artificial neural
networks. Journal of Thermal Analysis and Calorimetry, 139(4), 2679-
2689.
[27] Bose, B. K. (2017). Artificial intelligence techniques in smart grid and
renewable energy systems—Some example applications. Proceedings
of the IEEE, 105(11), 2262-2273.
[28] Zor, K., Timur, O., & Teke, A. (2017, June). A state-of-the-art review
of artificial intelligence techniques for short-term electric load
forecasting. In 2017 6th International Youth Conference on Energy
(IYCE) (pp. 1-7). IEEE.
[29] Maleki, A., Elahi, M., Assad, M. E. H., Nazari, M. A., Shadloo, M. S.,
& Nabipour, N. (2020). Thermal conductivity modeling of nanofluids
with ZnO particles by using approaches based on artificial neural
networks and MARS. Journal of Thermal Analysis and Calorimetry, 1-
12.
[30] Turban, E. Frenzel, LE. (1992). Expert systems and applied artificial
intelligence. New Jersey: Prentice-Hall;
[31] Kalaitzakis K, Koutroulis E, Vlachos V. (2003). Development of a data
acquisition system for remote monitoring of renewable energy systems.
Measurement;34(2):75–83.
[32] Foreroa, N., Hernándezb, J., Gordillo, G. (2006). Development of a
monitoring system for a PV solar plant. Energy Convers
Manag;47:2329–36.
[33] Dalton GJ, Lockington DA, Baldock TE (2009). Feasibility analysis of
renewable energy supply options for a grid-connected large hotel.
Renew Energy;34(4):955–64.
[34] Ustun, TS, Ozansoy C, Zayegh A. (2011). Recent developments in
microgrids and example cases around the world—a review. Renew
Sust Energy Rev;15(8):4030–41.
[35] Belu, R. (2013). Artificial intelligence techniques for solar energy and
photovoltaic applications. In Handbook of Research on Solar Energy
Systems and Technologies (pp. 376-436). IGI Global.
[36] Kolbjørnsrud, V., Amico, R., & Thomas, R. J. (2016). The promise of
artificial intelligence.
[37] Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the
fairest in the land? On the interpretations, illustrations, and
implications of artificial intelligence. Business Horizons, 62(1), 15-25.
[38] Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (1984). Machine
learning: An artificial intelligence approach. Berlin: Springer-Verlag.
[39] Jackson, P. (1986) Introduction to expert systems. USA: Addison-
Wesley.
[40] Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F.,
... & Parikh, B. (2019). Translating cancer genomics into precision
medicine with artificial intelligence: applications, challenges, and
future perspectives. Human genetics, 138(2), 109-124.
[41] Morrison, C., Cutrell, E., Dhareshwar, A., Doherty, K., Thieme, A., &
Taylor, A. (2017, October). Imagining Artificial Intelligence
Applications with People with Visual Disabilities using Tactile
Ideation. In Proceedings of the 19th International ACM SIGACCESS
Conference on Computers and Accessibility (pp. 81-90). ACM.
[42] Kokina, J., & Davenport, T. H. (2017). The emergence of artificial
intelligence: How automation is changing auditing. Journal of
Emerging Technologies in Accounting, 14(1), 115-122.
[43] Hildmann, H. (2018). Computer Games and Artificial Intelligence.
Encyclopedia of Computer Graphics and Games, 1-11.
[44] Bannerjee, G., Sarkar, U., Das, S., & Ghosh, I. (2018). Artificial
Intelligence in Agriculture: A Literature Survey. International Journal
of Scientific Research in Computer Science Applications and
Management Studies., 7(3), 1-6.
[45] Patrício, D. I., & Rieder, R. (2018). Computer vision and artificial
intelligence in precision agriculture for grain crops: A systematic
review. Computers and Electronics in Agriculture, 153, 69-81.
[46] Barth, R. (2018). Vision Principles for Harvest Robotics. Sowing
Artificial Intelligence in Agriculture (Doctoral dissertation, Ph. D.
Thesis, Wageningen University, Wageningen, The Netherlands).
[47] Shadrin, D., Menshchikov, A., Ermilov, D., & Somov, A. (2019).
Designing Future Precision Agriculture: Detection of Seeds
Germination Using Artificial Intelligence on a Low-Power Embedded
System. IEEE Sensors Journal.
[48] Kodama, T., & Hata, Y. (2018, October). Development of
Classification System of Rice Disease Using Artificial Intelligence. In
2018 IEEE International Conference on Systems, Man, and
Cybernetics (SMC) (pp. 3699-3702). IEEE.
[49] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y.
(2017). Artificial intelligence in healthcare: past, present, and future.
Stroke and vascular neurology, 2(4), 230-243.
[50] Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine.
Metabolism, 69, S36-S40.
[51] Williams, S. R., Taylor, M. K., & George, K. (2017). Improving
Biometric and Forensic Technology: The Future of Research Datasets
Symposium Report (No. NIST Interagency/Internal Report (NISTIR)-
8156).
[52] Singh, S. (2019). The role of speech technology in biometrics,
forensics, and man-machine interface. International Journal of
Electrical and Computer Engineering, 9(1), 281.
[53] Ali, A. M., & Angelov, P. (2017). Applying computational intelligence
to community policing and forensic investigations. In Community
Policing-A European Perspective (pp. 231-246). Springer, Cham.
[54] Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence:
foundations of computational agents. Cambridge University Press.
[55] Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern
approach. Malaysia; Pearson Education Limited.
[56] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y.
(2017). Artificial intelligence in healthcare: past, present, and future.
Stroke and vascular neurology, 2(4), 230-243.
[57] Ahmadi, M. H., Nazari, M. A., Ghasempour, R., Madah, H., Shafii, M.
B., & Ahmadi, M. A. (2018). Thermal conductivity ratio prediction of
Al2O3/water nanofluid by applying connectionist methods. Colloids
and Surfaces A: Physicochemical and Engineering Aspects, 541, 154-
164.
[58] Loni, R., Kasaeian, A., Shahverdi, K., Asli-Ardeh, E. A., Ghobadian,
B., & Ahmadi, M. H. (2017). ANN model to predict the performance
of a parabolic dish collector with a tubular cavity receiver. Mechanics
& Industry, 18(4), 408.
[59] Ahmadi, M. H., Sadeghzadeh, M., Maddah, H., Solouk, A., Kumar, R.,
& Chau, K. W. (2019). Precise, smart model for estimating dynamic
viscosity of SiO2/ethylene glycol–water nanofluid. Engineering
Applications of Computational Fluid Mechanics, 13(1), 1095-1105.
[60] Kahani, M., Ahmadi, M. H., Tatar, A., & Sadeghzadeh, M. (2018).
Development of multilayer perceptron artificial neural network (MLP-
ANN) and least square support vector machine (LSSVM) models to
predict Nusselt number and pressure drop of TiO2/water nanofluid
flows through non-straight pathways. Numerical Heat Transfer, Part A:
Applications, 74(4), 1190-1206.
[61] Rezaei, M. H., Sadeghzadeh, M., Alhuyi Nazari, M., Ahmadi, M. H.,
& Astaraei, F. R. (2018). Applying GMDH artificial neural network in
modeling CO2 emissions in four nordic countries. International Journal
of Low-Carbon Technologies, 13(3), 266-271.
[62] Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017).
Renewable energy: Present research and future scope of Artificial
Intelligence. Renewable and Sustainable Energy Reviews, 77, 297-
317.
[63] Maleki, A., Nazari, M. A., & Pourfayaz, F. (2020). Harmony search
optimization for optimum sizing of hybrid solar schemes based on a
battery storage unit. Energy Reports.Mahdavinejad, M. S., Rezvan, M.,
Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine
learning for Internet of Things data analysis: A survey. Digital
Communications and Networks, 4(3), 161-175.
[64] Linda Misauer (2017). IoT, Big Data, and AI – the New 'Superpowers'
In the Digital Universe. https://www.business2community.com/big-
data/iot-big-data-ai-new-superpowers-digital-universe-01926411.
Date accessed: October 23, 2019.
[65] Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical
analysis and data mining applications. Academic Press.
[66] Ahmadi, M. H., Dehghani Madvar, M., Sadeghzadeh, M., Rezaei, M.
H., Herrera, M., & Shamshirband, S. (2019). Current status
investigation and predicting carbon dioxide emission in Latin
American countries by connectionist models. Energies, 12(10), 1916.
[67] Ramezanizadeh, M., Nazari, M. A., Ahmadi, M. H., Lorenzini, G., &
Pop, I. (2019). A review of the applications of intelligence methods in
predicting the thermal conductivity of nanofluids. Journal of Thermal
Analysis and Calorimetry, 138(1), 827-843.
6. [68] Mellit, A. (2008). Artificial Intelligence technique for modelling and
forecasting of solar radiation data: a review. International Journal of
Artificial intelligence and soft computing, 1(1), 52-76.
[69] Rehman S, Mohandes M. Artificial neural network estimation of global
solar radiation using air temperature and relative humidity. Energy
Policy 2008;36(2):571–6.
[70] Mellit, A., & Pavan, A. M. (2010). A 24-h forecast of solar irradiance
using artificial neural network: Application for performance prediction
of a grid-connected PV plant at Trieste, Italy. Solar Energy, 84(5), 807-
821.
[71] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N.
A., & Arshad, H. (2018). State-of-the-art in artificial neural network
applications: A survey. Heliyon, 4(11), e00938.
[72] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N.
A., & Arshad, H. (2018). State-of-the-art in artificial neural network
applications: A survey. Heliyon, 4(11), e00938.
[73] Mellita A, Kalogirou SA, Hontoria L, Shaarid S. Artificial intelligence
techniques for sizing photovoltaic systems: a review. Renew Sust
Energy Rev 2009;13(2):406–19.
[74] Zhao H, Magoulès F. A review on the prediction of building energy
consumption. Renew Sust Energy Rev 2012;16(6):3586–92.
[75] Mellit, A., & Pavan, A. M. (2010). A 24-h forecast of solar irradiance
using artificial neural network: Application for performance prediction
of a grid-connected PV plant at Trieste, Italy. Solar Energy, 84(5), 807-
821.
[76] Ahmadi, M. H., Baghban, A., Sadeghzadeh, M., Hadipoor, M., &
Ghazvini, M. (2020). Evolving connectionist approaches to compute
the thermal conductivity of TiO2/water nanofluid. Physica A:
Statistical Mechanics and its Applications, 540, 122489.
[77] Sadeghzadeh, M., Ahmadi, M. H., Kahani, M., Sakhaeinia, H., Chaji,
H., & Chen, L. (2019). Smart modeling by using artificial intelligent
techniques on the thermal performance of flat‐plate solar collectors
using nanofluid. Energy Science & Engineering, 7(5), 1649-1658.
[78] Ramezanizadeh, M., & Alhuyi Nazari, M. (2019). Modeling thermal
conductivity of Ag/water nanofluid by applying a mathematical
correlation and artificial neural network. International Journal of Low-
Carbon Technologies, 14(4), 468-474.
[79] Ahmadi, M. H., Tatar, A., Nazari, M. A., Ghasempour, R., Chamkha,
A. J., & Yan, W. M. (2018). Applicability of connectionist methods to
predict thermal resistance of pulsating heat pipes with ethanol by using
neural networks. International Journal of Heat and Mass Transfer, 126,
1079-1086.
[80] Pourkiaei, S. M., Ahmadi, M. H., & Hasheminejad, S. M. (2016).
Modeling and experimental verification of a 25W fabricated PEM fuel
cell by parametric and GMDH-type neural network. Mechanics &
Industry, 17(1), 105.
[81] Toghyani, S., Ahmadi, M. H., Kasaeian, A., & Mohammadi, A. H.
(2016). Artificial neural network, ANN-PSO, and ANN-ICA for
modeling the Stirling engine. International Journal of Ambient Energy,
37(5), 456-468.
[82] Zhao 2012
[83] Mellita A, Kalogirou SA. Artificial intelligence techniques for
photovoltaic applications: a review. Prog Energy Combust Sci
2008;34(5):574–632.
[84] Ji W, Chee KC. (2011). Prediction of hourly solar radiation using a
novel hybrid model of ARMA and TDNN. Sol Energy;85(5):808–17.
[85] Wang, N., Maleki, A., Alhuyi Nazari, M., Tlili, I., & Safdari Shadloo,
M. (2020). Thermal conductivity modeling of nanofluids contain MgO
particles by employing different approaches. Symmetry, 12(2), 206.
[86] Naeimi, A., Ahmadi, M. H., Sadeghzadeh, M., & Kasaeian, A. (2019).
Optimum arrangement of two-stage plug and concentrate recycling RO
systems using thermodynamic and exergy analysis. International
Journal of Numerical Methods for Heat & Fluid Flow.
[87] Ogliari E, Grimaccia F, Leva S, Mussetta M. (2013). Hybrid predictive
models for accurate forecasting in PV systems. Energies;6(4):1918–29.
[88] Bouzerdoum M, Mellit A, Pavan A.M. (2013). A hybrid model
(SARIMA–SVM) for short-term power forecasting of a small-scale
grid-connected photovoltaic plant. Sol Energy;98:226–35.
[89] Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petkovic´
D, Sudheer C. (2015). A support vector machine–firefly algorithm-
based model for global solar radiation prediction. Sol
Energy;115(9):632–44.
[90] Amirkhani S, Nasirivatan S, Kasaeian AB, Hajinezhad A. (2015) ANN
and ANFIS models to predict the performance of solar chimney power
plants. Renew energy;83:579–607.
[91] Mandal P, Madhira STS, Ul-haque A, Meng J, Pineda RL. Forecasting
power output of solar photovoltaic system using wavelet transform and
artificial intelligence techniques. Procedia Comput Sci 2012;12:332–7.
[92] Mellit, A., & Kalogirou, S. A. (2011). ANFIS-based modelling for
photovoltaic power supply system: A case study. Renewable energy,
36(1), 250-258.
[93] Zeng J, Qiao W. Short-term solar power prediction using a support
vector machine. Renw Energy 2013;52:118–27.
[94] Sharma, N., Sharma, P., Irwin, D., & Shenoy, P. (2011, October).
Predicting solar generation from weather forecasts using machine
learning. In 2011 IEEE international conference on smart grid
communications (SmartGridComm) (pp. 528-533). IEEE.
[95] Monteiro, C., Santos, T., Fernandez-Jimenez, L., Ramirez-Rosado, I.,
& Terreros-Olarte, M. (2013). Short-term power forecasting model for
photovoltaic plants based on historical similarity. Energies, 6(5), 2624-
2643.
[96] Li, Z., Rahman, S. M., Vega, R., & Dong, B. (2016). A hierarchical
approach using machine learning methods in solar photovoltaic energy
production forecasting. Energies, 9(1), 55.
[97] Atia, D. M., Fahmy, F. H., Ahmed, N. M., & Dorrah, H. T. (2012).
Optimal sizing of a solar water heating system based on a genetic
algorithm for an aquaculture system. Mathematical and Computer
Modelling, 55(3-4), 1436-1449.
[98] Ramezanizadeh, M., Ahmadi, M. H., Nazari, M. A., Sadeghzadeh, M.,
& Chen, L. (2019). A review of the utilized machine learning
approaches for modeling the dynamic viscosity of nanofluids.
Renewable and Sustainable Energy Reviews, 114, 109345.
[99] Ahmadi, M. H., Ahmadi, M. A., Sadatsakkak, S. A., & Feidt, M.
(2015). The connectionist intelligent model estimates the output power
and torque of the stirling engine. Renewable and Sustainable Energy
Reviews, 50, 871-883.
[100]Almonacid, F., Fernández, E. F., Rodrigo, P., Pérez-Higueras, P. J., &
Rus-Casas, C. (2013). Estimating the maximum power of a high
concentrator photovoltaic (HCPV) module using an artificial neural
network. Energy, 53, 165-172.
[101]Kumar, P., Jain, G., & Palwalia, D. K. (2015, August). Genetic
algorithm-based maximum power tracking in solar power generation.
In 2015 International Conference on Power and Advanced Control
Engineering (ICPACE) (pp. 1-6). IEEE.
[102]Mellit, A., & Pavan, A. M. (2010). A 24-h forecast of solar irradiance
using artificial neural network: Application for performance prediction
of a grid-connected PV plant at Trieste, Italy. Solar Energy, 84(5), 807-
821.
[103]Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petkovic´
D, Sudheer C. (2015). A support vector machine–firefly algorithm-
based model for global solar radiation prediction. Sol
Energy;115(9):632–44.
[104]Mallesham, G., S. Mishra, and A. N. Jha. "Automatic generation
control of microgrid using artificial intelligence techniques." 2012
IEEE Power and Energy Society General Meeting. IEEE, 2012.
[105]Navya, R., et al. "A predictive model for analyzing electric
consumption patterns in solar photo voltaic micro grid." 2017 2nd
International Conference on Computational Systems and Information
Technology for Sustainable Solution (CSITSS). IEEE, 2017.
[106]Cabrera, Wellington, Driss Benhaddou, and Carlos Ordonez. "Solar
power prediction for smart community microgrid." 2016 IEEE
International Conference on Smart Computing (SMARTCOMP).
IEEE, 2016.
[107]Rodríguez, Fermín, et al. "Predicting solar energy generation through
artificial neural networks using weather forecasts for microgrid
control." Renewable Energy 126 (2018): 855-864.
[108]Kofinas, P., A. I. Dounis, and G. A. Vouros. "Fuzzy Q-Learning for
multi-agent decentralized energy management in microgrids." Applied
energy 219 (2018): 53-67.