This document discusses feature selection and optimization of artificial neural networks for short term load forecasting. It begins with an introduction to load forecasting and its importance, as well as common techniques. The objective is to review factors that influence short term load forecasting and compare techniques. The model uses artificial neural networks to study how temperature, dew point, wind and humidity each impact peak load forecasting individually. Results show that a hybrid model using all factors reduces errors more than models using single factors alone. Overall conclusions are that load forecasting always has some uncertainty, but combining meteorological and human behavior factors improves accuracy.
The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This chapter deals with Load forecasting of different power system parts which includes the generation, transmission and distribution systems. This slide is specifically prepared for ASTU 5th year power and control engineering students.
This slide is an introductory part of the course Computer Application in Power system. it will describe the basic tasks of a computer and different computer application areas.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
Using statistical and machine learning techniques to forecast the PV solar power, which can be implemented for: • Managing the economic dispatch, unit commitment, and trading of PV solar power generations with other conventional generations; • Using with situational awareness tools to manage the ramp limitation; Optimal energy management of energy storage systems; • Voltage regulator settings on feeders with PV distributed generation.
Embedded Applications of MS-PSO-BP on Wind/Storage Power ForecastingTELKOMNIKA JOURNAL
Higher proportion wind power penetration has great impact on grid operation and dispatching,
intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm
of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network)
is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural
network training sample, PSO-BP (particle swarm combined with back propagation neural network) is
employed on prediction error dynamic revision. From the angle of root mean square error (RMSE), the
mean absolute error (MAE) and convergence rate, analysis and comparison of several intelligent
algorithms (BP, RBP, PSO-BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the
proposed prediction method. Further, due to the physical function of energy storage in improving accuracy
of schedule pre-fabrication, a mathematical statistical method is proposed to determine the optimal
capacity of the storage batteries in power forecasting based on the historical statistical data of wind farm.
Algorithm feasibility is validated by application of experiment simulation and comparative analysis.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Qualifying combined solar power forecasts in ramp events' perspectiveMohamed Abuella
Applying a qualifying metric for solar power forecasts to assess their capability to predict the ramp events, especially by the combined forecasts, then those forecasts can be implemented for: Managing high ramp-rates of PV solar power generation; Optimal energy management of energy storage systems; Voltage regulator settings on feeders with PV distributed generation.
Short Term Load Forecasting Using Multi Layer Perceptron IJMER
Load forecasting is the method for prediction of Electrical load. Short term load forecasting is
one of the important concerns of power system and accurate load forecasting is essential for managing
supply and demand of electricity. The basic objective of STLF is to predict the near future load for example
next hour load prediction or next day load prediction etc….There are various factors which influence the
behaviour of the consumer load. The factors that we consider in this paper are Load,Temperature, humidity,
time. The ANN is used to learn the relationship among past, current and future parameters like load, temp. In
this paper we are using Multi parameter regression and comparing the results with the Artificial Neural
network output. Finally, outcomes of the approaches are evaluated and compared by means of the Mean
absolute Percentage error (MAPE).ANN outcomes are more fairly
accurate to the actual loads than those of conventional methods. So it can be considered as the suitable tool
to deal with STLF problems.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
This chapter deals with Load forecasting of different power system parts which includes the generation, transmission and distribution systems. This slide is specifically prepared for ASTU 5th year power and control engineering students.
This slide is an introductory part of the course Computer Application in Power system. it will describe the basic tasks of a computer and different computer application areas.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
Using statistical and machine learning techniques to forecast the PV solar power, which can be implemented for: • Managing the economic dispatch, unit commitment, and trading of PV solar power generations with other conventional generations; • Using with situational awareness tools to manage the ramp limitation; Optimal energy management of energy storage systems; • Voltage regulator settings on feeders with PV distributed generation.
Embedded Applications of MS-PSO-BP on Wind/Storage Power ForecastingTELKOMNIKA JOURNAL
Higher proportion wind power penetration has great impact on grid operation and dispatching,
intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm
of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network)
is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural
network training sample, PSO-BP (particle swarm combined with back propagation neural network) is
employed on prediction error dynamic revision. From the angle of root mean square error (RMSE), the
mean absolute error (MAE) and convergence rate, analysis and comparison of several intelligent
algorithms (BP, RBP, PSO-BP, MS-BP, MS-RBP, MS-PSO-BP) are done to verify the availability of the
proposed prediction method. Further, due to the physical function of energy storage in improving accuracy
of schedule pre-fabrication, a mathematical statistical method is proposed to determine the optimal
capacity of the storage batteries in power forecasting based on the historical statistical data of wind farm.
Algorithm feasibility is validated by application of experiment simulation and comparative analysis.
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Qualifying combined solar power forecasts in ramp events' perspectiveMohamed Abuella
Applying a qualifying metric for solar power forecasts to assess their capability to predict the ramp events, especially by the combined forecasts, then those forecasts can be implemented for: Managing high ramp-rates of PV solar power generation; Optimal energy management of energy storage systems; Voltage regulator settings on feeders with PV distributed generation.
Short Term Load Forecasting Using Multi Layer Perceptron IJMER
Load forecasting is the method for prediction of Electrical load. Short term load forecasting is
one of the important concerns of power system and accurate load forecasting is essential for managing
supply and demand of electricity. The basic objective of STLF is to predict the near future load for example
next hour load prediction or next day load prediction etc….There are various factors which influence the
behaviour of the consumer load. The factors that we consider in this paper are Load,Temperature, humidity,
time. The ANN is used to learn the relationship among past, current and future parameters like load, temp. In
this paper we are using Multi parameter regression and comparing the results with the Artificial Neural
network output. Finally, outcomes of the approaches are evaluated and compared by means of the Mean
absolute Percentage error (MAPE).ANN outcomes are more fairly
accurate to the actual loads than those of conventional methods. So it can be considered as the suitable tool
to deal with STLF problems.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Electrical load forecasting using Hijri causal eventsMaged M. Eljazzar
1. M. M. Elgazzar and E. E. Hemayed, "Electrical load forecasting using Hijri causal events," 2016 Eighteenth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 2016, pp. 902-906.
Electrical load forecasting using Hijri causal eventsMaged M. ElJazzar
1. M. M. Elgazzar and E. E. Hemayed, "Electrical load forecasting using Hijri causal events," 2016 Eighteenth International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 2016, pp. 902-906.
Daily Peak Load Forecast Using Artificial Neural NetworkIJECEIAES
The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.
Short term residential load forecasting using long short-term memory recurre...IJECEIAES
Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Short term load forecasting system based on support vector kernel methodsijcsit
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
A framework for cloud cover prediction using machine learning with data imput...IJECEIAES
The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): “physical” and “statistical”. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Similar to Feature Selection and Optimization of Artificial Neural Network for Short Term Load Forecasting (20)
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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UI automation Sample
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Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
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Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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And...
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Generating a custom Ruby SDK for your web service or Rails API using Smithy
Feature Selection and Optimization of Artificial Neural Network for Short Term Load Forecasting
1. Feature Selection and Optimization of
Artificial Neural Network for Short Term
Load Forecasting
Elsayed E. Hemayed and Maged M. Eljazzar
Computer Engineering Dept.
Faculty of Engineering
Cairo University, Egypt
mmjazzar@ieee.org
2016 Eighteenth International Middle-East Power Systems Conference (MEPCON)
December 27-29, 2016 - Helwan University, Cairo – Egypt
1
3. Introduction
– Why Load forecasting is important ?
– Types of load forecasting.
– Machine learning techniques (ANN, SVM).
– Statistical techniques (ARIMA, regression).
– Load forecasting parameters.
– Data sets.
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4. Objective
– Our goal is to assist researchers in their work with a
detailed review of load forecasting parameters
– Besides presenting an overview of load forecasting
techniques in short term load forecasting (STLF) in
different scenarios.
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5. Literature review
– Short term load forecasting factors
• Temperature, Humidity, and Precipitation
• Accumulative effect of sunny days.
• Economic factors (electricity price).
– Short term load forecasting Techniques
• Statistical: ARIMA, Regression analysis.
• Artificial intelligence: ANN, SVM, and fuzzy logic.
• Deep learning.
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6. Load forecasting factors
– Location: the demographic location and the culture of the
country.
– forecasting in the Capital city differs than forecasting in a
small city.
– The impact of human activities
• Daily Resolution: such as Ramadan.
• Monthly Resolution : the urban development
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7. Classification of load forecasts
time Weather Economic Land use Cycle Horizon
VSTLF Optional Optional Optional <1
hour
1 day
STLF Required Optional Optional 1 Day 2 weeks
MTLF Simulated Required Optional 1
month
3 years
LTLF Simulated Simulated Required 1 year 30 years
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8. Load forecasting factors
– In some countries, electricity price varied during the day.
It is cheaper at night than at day.
– Because people tend to use electricity for heat storage
equipment at night and during day, use stored heat for
warming the rooms
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10. Model
– ANN are used to study each individual components
according to their influence on the load forecasting.
– The aim is to study the relationship between input and
peak load
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12. Forecasting errors using each factor
independently with peak load
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Factor
included
MAPE MAE RMSE
--------- 0.9902853 22.30397 33.78119
Temp 0.9277951 20.90214 31.68409
Dew Temp 0.9200192 20.73557 30.05431
Wind 0.9802346 21.96305 33.48497
Humidity 0.9533866 21.51869 31.83082
13. Model
– Model 1 represents the temperature only.
– Model 2 represents temperature and dew temperature.
– Model 3 represents temperature, dew temperature and
wind.
– Model 4 represents temperature, dew temperature and
humidity.
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14. Forecasting errors using each factor
independently with peak load
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Model MAPE MAE RMSE
Model 1 0.9277951 20.90214 31.68409
Model 2 0.2990653 6.835276 10.45197
Model 3 0.2928311 6.741303 10.25782
Model 4 0.2734582 6.231536 9.319102
15. Conclusions
– Load forecasting results always contain certain degree of
variance. This variance due to the random Nature of the
load and human behavior.
– The forecasting errors (RMSE, MAPE, MAE) are reduced by
more than half using the hybrid model.
– This work needs to be extended to cover very short term
load forecasting and covers more scenarios;
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16. Thank you for further questions
mmjazzar@ieee.org
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