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.
Electrical load forecasting through long short term memoryIJEECSIAES
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a
large amount of electrical energy cannot be stored. For the proper
functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis,
yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM)
neural networks, a recurrent neural network capable of handling both longterm and short-term dependencies of data sets, for predicting load that is to
be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
Electrical load forecasting through long short term memorynooriasukmaningtyas
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis, yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM) neural networks, a recurrent neural network capable of handling both long-term and short-term dependencies of data sets, for predicting load that is to be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
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.
Electrical load forecasting through long short term memoryIJEECSIAES
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a
large amount of electrical energy cannot be stored. For the proper
functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis,
yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM)
neural networks, a recurrent neural network capable of handling both longterm and short-term dependencies of data sets, for predicting load that is to
be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
Electrical load forecasting through long short term memorynooriasukmaningtyas
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis, yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM) neural networks, a recurrent neural network capable of handling both long-term and short-term dependencies of data sets, for predicting load that is to be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
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 %.
INTERVAL TYPE-2 FUZZY NEURAL NETWORKS FOR SHORT-TERM ELECTRIC LOAD FORECASTIN...ijsc
This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides
additional degrees of freedom for handling more uncertainties for improving prediction accuracy and reducing cost. The IT2FL comprises five components which include; the fuzzification unit, the knowledge base, the inference engine, the type reducer and the defuzzification unit. Gaussian membership function is
used to show the degree of membership of the input variables. The lower and upper membership functions (fired rules) as well as their consequent coefficients of IT2FL are fed into a (NN) which produces a crisp value coresponding to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained to
optimize parameters of membership function (MF) so as to produce an output with minimum error function with the purpose of improving forecasting performance of IT2FLS models. The IT2FNN system has the ability to overcome the limitations of individual technique and enhances their strengths to handle electric load forecasting. The IT2FNN is applied for STELF in Akwa Ibom State-Nigeria. The result of performance
of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MSE of 0.00123, 0.00185 and 0.00247 respectively. Also, the results of forecasting are compared using RMSE of 0.035, 0.043 and 0.035 respectively, indicating a best accurate forecasting with IT2FNN. In addition, the result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MAPE of 1.5%, 3% and 4.5% respectively. Simulation results show that the IT2FNN approach takes advantages of accuracy and efficiency and performs better in prediction than IT2FL and
T1FL methods in power load forecasting task. .
Interval Type-2 Fuzzy Neural Networks for Short Term Electric Load Forecastin...ijsc
This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction accuracy and reducing cost. The IT2FL comprises five components which include; the fuzzification unit, the knowledge base, the inference engine, the type reducer and the defuzzification unit. Gaussian membership function is used to show the degree of membership of the input variables. The lower and upper membership functions (fired rules) as well as their consequent coefficients of IT2FL are fed into a (NN) which produces a crisp
value coresponding to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained to optimize parameters of membership function (MF) so as to produce an output with minimum error function with the purpose of improving forecasting performance of IT2FLS models. The IT2FNN system has the ability to overcome the limitations of individual technique and enhances their strengths to handle electric load forecasting. The IT2FNN is applied for STELF in Akwa Ibom State-Nigeria. The result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MSE of 0.00123, 0.00185 and 0.00247 respectively. Also, the results of forecasting are compared using RMSE of 0.035, 0.043 and 0.035 respectively, indicating a best accurate forecasting with IT2FNN. In addition, the result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MAPE of 1.5%, 3% and 4.5% respectively. Simulation results show that the IT2FNN approach takes advantages of accuracy and efficiency and performs better in prediction than IT2FL and T1FL methods in power load forecasting task.
System operators face a proliferation of power electronics
interfaced devices such as HVDC transmission lines,
wind and solar generation in their grids. Depending on
the jurisdiction, the instantaneous share of electrical
energy produced from renewable energy sources
occasionally reaches 150%. However, to operate a power
system with sustained high levels of renewable energy,
several operational challenges need to be addressed. The
goal of this survey paper, which is one of the products
of CIGRE joint working group C2/B4.38, is to identify
such challenges. To this extend, extensive literature
review and survey among and discussions with system
operators throughout the world were performed.
This paper identified several operational challenges that
were validated by system operators. These challenges
are grouped in the following three categories: (i) new
behavior of the power system, (ii) new operation of the
power system and (iii) lack of voltage and frequency
support. For each of the identified challenge, a
description, practical examples and relevant references
are provided.
From interconnections of local electric power systems to Global Energy Interc...Power System Operation
The interconnections of electric power systems are developed for the economic benefits and in order to increase
the overall power supply reliability and quality level. Development of power industry shows the positive effects in operation
of the country-wide electric power systems and international interconnections. Creation of World Energy System or, by the
other words, Global Energy Interconnection is objective trend on the way of expansion of international and intercontinental
electric power interconnections. Several important aspects of above mentioned problems are discussed in this paper.
Keywords: Electric Power Systems, Power Interconnections, Global Energy Interconnection.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Characterization of electricity demand based on energy consumption data from ...IJECEIAES
The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic.
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.
Before we kick-off a new line-up of insightful studies and conversations on energy this 2021, we take a snapshot of the previous working papers which were featured last year.
These studies were produced under the Access to Sustainable Energy Programme-Clean Energy Living Laboratories (ASEP-CELLs) project implemented by the Ateneo School of Government (ASOG), and funded by the European Union.
To receive updates on our latest events and publications, please subscribe to our mailing list through this link: http://bit.ly/ASEPCELLsMailingList
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
A number of factors are contributing to increases in renewable energy production in the United
States (and beyond). These factors include rapidly declining costs of electricity produced from
renewable energy sources, regulatory and policy obligations and incentives, and moves to reduce
pollution from fossil fuel-based power generation, including greenhouse gas emissions. While
not all renewable energy sources are variable, two such technologies – wind and solar PV –
currently dominate the growth of renewable electricity production. The production from wind
and solar PV tries to capture the freely available but varying amount of wind and solar
irradiance. As the share of electricity produced from variable renewable resources grows, so does
the need to integrate these resources in a cost-effective manner, i.e., to ensure that total
electricity production from all sources including variable renewable generation equals electricity
demand in real time. Also, a future electric system characterized by a rising share of renewable
energy will likely require concurrent changes to the existing transmission and distribution
(T&D) infrastructure. While this report does not delve into that topic, utilities, grid operators
and regulators must carefully plan for needed future investments in T&D, given the lead times
and complexities involved.
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 %.
INTERVAL TYPE-2 FUZZY NEURAL NETWORKS FOR SHORT-TERM ELECTRIC LOAD FORECASTIN...ijsc
This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides
additional degrees of freedom for handling more uncertainties for improving prediction accuracy and reducing cost. The IT2FL comprises five components which include; the fuzzification unit, the knowledge base, the inference engine, the type reducer and the defuzzification unit. Gaussian membership function is
used to show the degree of membership of the input variables. The lower and upper membership functions (fired rules) as well as their consequent coefficients of IT2FL are fed into a (NN) which produces a crisp value coresponding to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained to
optimize parameters of membership function (MF) so as to produce an output with minimum error function with the purpose of improving forecasting performance of IT2FLS models. The IT2FNN system has the ability to overcome the limitations of individual technique and enhances their strengths to handle electric load forecasting. The IT2FNN is applied for STELF in Akwa Ibom State-Nigeria. The result of performance
of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MSE of 0.00123, 0.00185 and 0.00247 respectively. Also, the results of forecasting are compared using RMSE of 0.035, 0.043 and 0.035 respectively, indicating a best accurate forecasting with IT2FNN. In addition, the result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MAPE of 1.5%, 3% and 4.5% respectively. Simulation results show that the IT2FNN approach takes advantages of accuracy and efficiency and performs better in prediction than IT2FL and
T1FL methods in power load forecasting task. .
Interval Type-2 Fuzzy Neural Networks for Short Term Electric Load Forecastin...ijsc
This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction accuracy and reducing cost. The IT2FL comprises five components which include; the fuzzification unit, the knowledge base, the inference engine, the type reducer and the defuzzification unit. Gaussian membership function is used to show the degree of membership of the input variables. The lower and upper membership functions (fired rules) as well as their consequent coefficients of IT2FL are fed into a (NN) which produces a crisp
value coresponding to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained to optimize parameters of membership function (MF) so as to produce an output with minimum error function with the purpose of improving forecasting performance of IT2FLS models. The IT2FNN system has the ability to overcome the limitations of individual technique and enhances their strengths to handle electric load forecasting. The IT2FNN is applied for STELF in Akwa Ibom State-Nigeria. The result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MSE of 0.00123, 0.00185 and 0.00247 respectively. Also, the results of forecasting are compared using RMSE of 0.035, 0.043 and 0.035 respectively, indicating a best accurate forecasting with IT2FNN. In addition, the result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MAPE of 1.5%, 3% and 4.5% respectively. Simulation results show that the IT2FNN approach takes advantages of accuracy and efficiency and performs better in prediction than IT2FL and T1FL methods in power load forecasting task.
System operators face a proliferation of power electronics
interfaced devices such as HVDC transmission lines,
wind and solar generation in their grids. Depending on
the jurisdiction, the instantaneous share of electrical
energy produced from renewable energy sources
occasionally reaches 150%. However, to operate a power
system with sustained high levels of renewable energy,
several operational challenges need to be addressed. The
goal of this survey paper, which is one of the products
of CIGRE joint working group C2/B4.38, is to identify
such challenges. To this extend, extensive literature
review and survey among and discussions with system
operators throughout the world were performed.
This paper identified several operational challenges that
were validated by system operators. These challenges
are grouped in the following three categories: (i) new
behavior of the power system, (ii) new operation of the
power system and (iii) lack of voltage and frequency
support. For each of the identified challenge, a
description, practical examples and relevant references
are provided.
From interconnections of local electric power systems to Global Energy Interc...Power System Operation
The interconnections of electric power systems are developed for the economic benefits and in order to increase
the overall power supply reliability and quality level. Development of power industry shows the positive effects in operation
of the country-wide electric power systems and international interconnections. Creation of World Energy System or, by the
other words, Global Energy Interconnection is objective trend on the way of expansion of international and intercontinental
electric power interconnections. Several important aspects of above mentioned problems are discussed in this paper.
Keywords: Electric Power Systems, Power Interconnections, Global Energy Interconnection.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Characterization of electricity demand based on energy consumption data from ...IJECEIAES
The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic.
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.
Before we kick-off a new line-up of insightful studies and conversations on energy this 2021, we take a snapshot of the previous working papers which were featured last year.
These studies were produced under the Access to Sustainable Energy Programme-Clean Energy Living Laboratories (ASEP-CELLs) project implemented by the Ateneo School of Government (ASOG), and funded by the European Union.
To receive updates on our latest events and publications, please subscribe to our mailing list through this link: http://bit.ly/ASEPCELLsMailingList
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
A number of factors are contributing to increases in renewable energy production in the United
States (and beyond). These factors include rapidly declining costs of electricity produced from
renewable energy sources, regulatory and policy obligations and incentives, and moves to reduce
pollution from fossil fuel-based power generation, including greenhouse gas emissions. While
not all renewable energy sources are variable, two such technologies – wind and solar PV –
currently dominate the growth of renewable electricity production. The production from wind
and solar PV tries to capture the freely available but varying amount of wind and solar
irradiance. As the share of electricity produced from variable renewable resources grows, so does
the need to integrate these resources in a cost-effective manner, i.e., to ensure that total
electricity production from all sources including variable renewable generation equals electricity
demand in real time. Also, a future electric system characterized by a rising share of renewable
energy will likely require concurrent changes to the existing transmission and distribution
(T&D) infrastructure. While this report does not delve into that topic, utilities, grid operators
and regulators must carefully plan for needed future investments in T&D, given the lead times
and complexities involved.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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Engr. Badar Ul Islam
Head, Department of Computer Science & Engineering
NFC-Institute of Engineering & Fertilizer Research, Faisalabad - Pakistan
Abstract
This paper picturesquely depicts the comparison
of different methodologies adopted for
predicting the load demand and highlights the
changing trend and values under new
circumstances using latest non analytical soft
computing techniques employed in the field of
electrical load forecasting. A very clear
advocacy about the changing trends from
conventional and obsolete to the modern
techniques is explained in very simple way. Load
forecast has been a central and an integral
process in the planning and operation of electric
utilities. Many techniques and approaches have
been investigated to tackle this problem in the
last two decades. These are often different in
nature and apply different engineering
considerations and economic analysis. Further a
clear comparison is also presented between the
past standard practices with the current
methodology of electrical load demand
forecasting. Besides all this, different important
points are highlighted which need special
attention while doing load forecasting when the
environment is competitive and deregulated one.
1.0 INTRODUCTION
Electrical Load Forecasting is the estimation for
future load by an industry or utility company.
Load forecasting is vitally important for the
electric industry in the deregulated economy. A
large variety of mathematical methods have been
developed for load forecasting. It has many
applications including energy purchasing and
generation, load switching, contract evaluation,
and infrastructure development.
Now a days, development in every sector is a
heading at a very rapid pace and in the same
pattern, the demand for power is also growing.
While speaking about electrical power, it is
important to understand that it has three main
sectors i.e. generation, transmission and
distribution. Electrical power generated by any
source is then transmitted through transmission
lines at different voltage level and then
distributed to different categories of consumers
later on. It is not as simple as described in few
words but every stage is a complete independent
system in itself. Effective load forecasts can help
to improve and properly plan these three fields of
power systems [1].
Accurate models for electric power load
forecasting are essential to the operation and
planning of a utility company. Load forecasting
helps an electric utility to make important
decisions including decisions on purchasing and
generating electric power, load switching, and
infrastructure development. Load forecasts are
extremely important for energy suppliers, ISOs,
financial institutions, and other participants in
electric energy generation, transmission,
distribution, and markets.
Over the past decade, many western nations have
begun major structural reforms of their
electricity markets. These reforms are aimed at
breaking up traditional regional monopolies and
replacing them with several generation and
distribution utilities that bid to sell or buy
electricity through a wholesale market. While the
rules of how various wholesale markets operate
differ, in each case it is hoped that the end result
is a decline in the price of electricity to end users
and a price that better reflects the actual costs
involved. To successfully operate in these new
markets electricity utilities face two complex
statistical problems: how to forecast both
electricity load and the wholesale spot price of
electricity. Failure to implement efficient
solutions to these two forecasting problems can
directly result in multimillion dollar losses
through uninformed trades in the wholesale
market.
Load forecasting is however a difficult task.
First, because the load series is complex and
exhibits several levels of seasonality: the load at
a given hour is dependent not only on the load at
the previous hour, but also on the load at the
same hour on the previous day, and on the load
at the same hour on the day with the same
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
www.IJCSI.org 504
2. denomination in the previous week. Secondly,
there are many important exogenous variables
that must be considered, especially weather-
related variables. It is relatively easy to get
forecast with about 10 % mean absolute error;
however, the cost of error are so high that
research could help to reduce it in a few percent
points would be amply justified [2].
2.0 ELECTRICAL LOAD
FORECASTING TYPES
The electricity supply industry requires to
forecast electricity demand with lead times that
range from the short term (a few minutes, hours,
or days ahead) to the long term (up to 20 years
ahead). Load forecasting has three techniques
shown in Figure 2.1:
Figure 2.1 Basic Load Forecasting Techniques
Short term electric load forecast spans the period
from one hour up to one week and it is mainly
utilized for power system operation studies,
losses reduction, voltage regulations, unit
commitment and maximizing the utility revenues
in the deregulated environment. Medium term
electric load forecast spans the period from one
week to several weeks, it is mainly utilized for
predicting the necessary power to purchase or
sell from other neighboring networks (inter-tie
exchanged power) and also the fuel required by
the utility in the near future. In short and medium
electric load forecast, it is required to know how
much power we will need and at what time of the
day; the information regarding where this
demand is required is not of a major concern [1].
3.0 ELECTRICAL LOAD
FORECASTING METHODS
A model or method is a mathematical description
of how the complex elements of a real-life
situation or problem might interplay at some
future date. In projecting electricity demand, a
method uses data on electricity prices, income,
population, the economy, and the growth rates
for each and then varies the mix according to
varying sets of assumptions. Different
assumptions produce different outcomes. The
relationships between electricity demand and the
multitude of factors that influence or affect
electricity demand are expressed in mathematical
equations called functions. A model is a
collection of functions. A function, in turn, is
made up of variables for which those factors
which change or can be changed. Independent
variables are those factors which influence the
demand for electricity, and the dependent
variable is electricity demand itself. In other
words, the demand for electricity depends on
population, income, prices, etc. Finally,
elasticities describe how much the dependent
variable (electricity demand) changes in sense to
small changes in the independent variables.
Elasticities are what the modeler uses to measure
consumer behavior.
Energy planners often speak of scenarios.
Hypothetical pictures of the future based on
different assumptions about economic or
political events. They make different projections
for each scenario. For example, a low growth
scenario might assume high energy prices and
slow population growth, while a high-growth
scenario would assume the opposite. These
scenarios allow planners to see how electricity
demand might change if the different assumed
economic and political events actually occur. All
of the forecasting methods are capable of looking
at different scenarios and do so by changing their
basic assumptions [5].
4.0 SHORT TERM LOAD
FORECASTING METHODS
Short-Term Load Forecasting is basically is a
load predicting system with a leading time of one
hour to seven days, which is necessary for
adequate scheduling and operation of power
systems. It has been an essential component of
Energy Management Systems (EMS). For proper
and profitable management in electrical utilities,
short-term load forecasting has lot of importance.
High forecasting accuracy and speed are the two
most important requirements of short-term load
forecasting and it is important to analyze the load
characteristics and identify the main factors
Long Term
1 year – 20
Year
Medium
Term
1 week – 10
weeks
Short Term
1 hr – 1 week
Electric Load
Forecasts
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
www.IJCSI.org 505
3. affecting the load. In electricity markets, the
traditional load affecting factors such as season,
day type and weather, electricity price that have
voluntary and may have a complicated
relationship with system load..
Various forecasting techniques have been
applied to short-term load forecasting to improve
accuracy and efficiency. In general, these
techniques can be classified as either traditional
or modern. Traditional statistical load forecasting
techniques, such as regression, time series,
pattern recognition, Kalman filters, etc., have
been used in practice for a long time, showing
the forecasting accuracy that is system
dependent. These traditional methods can be
combined using weighted multi-model
forecasting techniques, showing adequate results
in practical systems. However, these methods
cannot properly represent the complex nonlinear
relationships that exist between the load and a
series of factors that influence it, which are
typically dependent on system changes (e.g.,
season or time of day).
The short term load forecasting methods are
• Similar Day Lookup Approach
• Regression Based Approach
• Time Series Analysis
• Artificial Neural Network
• Expert System
• Fuzzy logic
• Support Vector Machines
4.1 Similar Day Look Up Approach
Similar day approach is based on searching
historical data of days of one, two or three years
having the similar characteristics to the day of
forecast. The characteristics include similar
weather conditions, similar day of the week or
date. The load of the similar day is considered as
the forecast. Now, instead of taking a single
similar day, forecasting is done through linear
combinations or regression procedures by taking
several similar days. The trend coefficients of the
previous years are extracted from the similar
days and forecast of the concern day is done on
their basis.
4.2 Regression Based Approach
The term "regression" was used in the nineteenth
century to describe a biological phenomenon,
namely that the progeny of exceptional
individuals tend on average to be less
exceptional than their parents and more like their
more distant ancestors.
Linear regression is a technique which examines
the dependent variable to specified independent.
The independent variables are firstly considered
because changes occur in them unfortunately. In
energy forecasting, the dependent variable is
usually demand or price of the electricity
because it depends on production which on the
other hand depends on the independent variables.
Independent variables are usually weather
related, such as temperature, humidity or wind
speed. Slope coefficients measure the sensitivity
of the dependent variable that how they changes
with the independent variable. Also, by
measuring how significant each independent
variable has historically been in its relation to the
dependent variable. The future value of the
dependent variable can be estimated. Essentially,
regression analysis attempts to measure the
degree of correlation between the dependent and
independent variables, thereby establishing the
latter’s predicted values[3].
Regression is the one of most widely used
statistical techniques. For electric load
forecasting, regression methods are usually used
to model the relationship of load consumption
and other factors such as weather, day type, and
customer class. There are several regression
models for the next day peak forecasting. Their
models contain deterministic influences such as
holidays, random variables influences such as
average loads, and exogenous influences such as
weather.
4.3 Time Series Analysis
Time series forecasting is based on the idea that
reliable predictions can be achieved by modeling
patterns in a time series plot, and then
extrapolating those patterns to the future. Using
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
www.IJCSI.org 506
4. historical data as input, time series analysis fits a
model according to seasonality and trend.
Time series models can be accurate in some
situations, but are especially complex and require
large amounts of historical data. Additionally,
careful efforts must made to ensure an accurate
time line through out data collection filtering
modeling and recall processes. Time series
analysis widely used in the martial management
for forecasting of customer demand for goods
services. Time series approaches are not widely
used for energy industry forecasting. Because
they typically do not take into account other key
factor, such as weather forecasts [3].
Time series have been used for longtime in such
fields as economics, digital signal processing, as
well as electric load forecasting. In particular,
ARMA (autoregressive moving average),
ARIMA (autoregressive integrated moving
average), ARMAX (autoregressive moving
average with exogenous variables), and
ARIMAX (autoregressive integrated moving
average with exogenous variables) are the most
used classical time series methods.
ARMA models are usually used for stationary
processes while ARIMA is an extension of
ARMA for non-stationary processes. ARMA and
ARIMA use the time and load as the only input
parameters. Since load generally depends on the
weather and time of the day, ARIMAX is the
most natural tool for load forecasting among the
classical time series models.
4.4 Artificial Neural Networks
Artificial Neural Networks are still at very early
stage electronic models based on the neural
structure of the brain. We know that the brain
basically learns from the experience. The
biological inspired methods are thought to be the
major advancement in the computational
industry. In a neural network, the basic
processing element is the neuron. These neurons
get input from some source, combine them,
perform all necessary operations and put the final
results on the output. Artificial neural networks
are developed since mid-1980 and extensively
applied. They have very successful applications
in pattern recognition and many other problems.
Forecasting is based on the pattern observed
from the past event and estimates the values for
the future. ANN is well suited to forecasting for
two reasons. First, it has been demonstrated that
ANN are able to approximate numerically any
continuous function to be desired accuracy. In
this case the ANN is seen as multivariate,
nonlinear and nonparametric methods. Secondly,
ANNs are date-driven methods, in the sense that
it is not necessary for the researcher to use
tentative modals and then estimate their
parameters. ANNs are able to automatically map
the relationship between input and output, they
learn this relationship and store this learning into
their parameters [3].
The first way is by repeatedly forecasting one
hourly load at a time. The second way is by
using a system with 24 NNs in parallel, one for
each hour of the day. Estimating a model that fits
the data so well that it ends by including some of
In Multi Layer Perceptron(MLP) structure of
neural network, the most commonly training
algorithm use is the back propagation algorithm.
These algorithms are iterative; some criteria
must be defined to stop the iterations. For this
either training is stopped after a fixed number of
iterations or after the error decreased below some
specified tolerance. This criterion is not
adequate, this insure that the model fits closely
to the training data but does not guarantee of
good performance they may lead to over-fitting
of the model. "Over-fitting" means the error
randomness in its structure, and then produces
poor forecasts. MLPs model is over-trained or
because it is too complex. One way to avoid
overtraining is by using cross-validation. The
sample set is split into a training set and a
validation set. The neural network parameters are
estimated on the training set, and the
performance of the model is tested, every few
iterations, on the validation set. When this
performance starts to deteriorate (which means
the neural network is over-fitting the training
data), the iterations are stopped, and the last set
of parameters to be computed is used to produce
the forecasts. Nowadays, other than MLPs to
avoid the problems of over-fitting and over-
parameterization, the ANNs architectures used
for prediction of electrical load are Functional
Link Network (FLN) model [1].
To use the ANN in electric load forecast
problems, distribution engineers should decide
upon a number of basic variables, these variables
include:
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
www.IJCSI.org 507
5. • Input variable to the ANN (load,
temperature…etc)
• Number of classes (weekday, weekend,
season…etc)
• What to forecast: hourly loads, next day
peak load, next day total load …etc
• Neural network structure (Feedforward,
number of hidden layer, number of
neuron in the hidden layer…etc)
• Training method and stopping criterion
• Activation functions
• Size of the training data
• Size of the test data
4.5 Expert Systems
Expert systems are new techniques that have
come out as a result of advances in the field of
artificial intelligence (AI) in the last two
decades. An expert system is a computer
program, which has the ability to act as an
expert. This means this computer program can
reason, explain, and have its knowledge base
expanded as new information becomes available
to it.
The load forecast model is built using the
knowledge about the load forecast domain from
an expert in the field. The "Knowledge
Engineer" extracts this knowledge from load
forecast (domain) expert which is called the
acquisition module component of the expert
system. This knowledge is represented as facts
and rules by using the first predicate logic to
represent the facts and IF-THEN production
rules. This representation is built in what is
called the knowledge base component of the
expert system. The search for solution or
reasoning about the conclusion drawn by the
expert system is performed by the "Inference
Engine" component of the expert system. For
any expert system it has to have the capability to
trace its reasoning if asked by the user. This
facility is built through an explanatory interface
component.
An example demonstrating this approach is the
rule-based algorithm which is based on the work
of two scientists Rahman and Baba. This
algorithm consists of functions that have been
developed for the load forecast model based on
the logical and syntactical relationship between
the weather and prevailing daily load shapes in
the form of rules in a rule-base. The rule-base
developed consists of the set of relationships
between the changes in the system load and
changes in natural and forced condition factors
that affect the use of electricity. The extraction of
these rules was done off-line, and was dependent
on the operator experience and observations by
the authors in most cases. Statistical packages
were used to support or reject some of the
possible relationships that have been observed
The rule-base consisted of all rules taking the IF-
THEN form and mathematical expressions. This
rule-base is used daily to generate the forecasts.
Some of the rules do not change over time, some
change very slowly while others change
continuously and hence are to be updated from
time to time [4].
4.6 Fuzzy Logic
Fuzzy logic based on the usual Boolean logic
which is used for digital circuit design. In
Boolean logic, the input may be the truth value
in the form of “0” and “1”. In case of fuzzy
logic, the input is related to the comparison
based on qualities. For example, we can say that
a transformer load may be “low” and “high”.
Fuzzy logic allows us to deduce outputs form
inputs logically. In this sense, the fuzzy facilitate
for mapping between inputs and outputs like
curve fitting [16].
The advantage of fuzzy logic is that there is no
need of mathematical models for mapping
between inputs and outputs and also there is no
need of precise or even noise free inputs. Based
on the general rules, properly designed fuzzy
logic systems are very strong for the electrical
load forecasting. There are many situations
where we require the precise outputs. After the
whole processing is done using the fuzzy logic,
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
www.IJCSI.org 508
6. the “defuzzification” is done to get the precise
outputs.
We know that power system load is influenced
by many load factors such weather, economic
and social activities and different load
components. By the analysis of historical load
data it is not easy to make the accurate forecast.
The use of these intelligent methods like fuzzy
logic and expert systems provide advantage on
other conventional methods. The numerical
aspects and uncertainties are suitable for the
fuzzy methodologies[5].
4.7 Support Vector Machines
Support Vector Machines (SVM) are the most
powerful and very recent techniques for the
solution of classification and regression
problems. This approach was come to known
from the work of Vapnik’s, his statistical
learning theory. Other from the neural network
and other intelligent systems, which try to define
the complex functions of the inputs, support
vector machines use the nonlinear mapping of
the data in to high dimensional features by using
the kernel functions mostly. In support vector
machines, we use simple linear functions to
create linear decision boundaries in the new
space. In the case of neural network, the problem
is in the choosing of architecture and in the case
of support vector machine, problems occurs in
choosing a suitable kernel.
Mohandes applied a method of support vector
machines for short-term electrical load
forecasting. He compares its method
performance with the autoregressive method.
The results indicate that SVMs compare
favorably against the autoregressive method.
Chen also proposed a SVM model to predict
daily load demand of a month. Lots of methods
are used in support vector machines [3].
5.0 MEDIUM AND LONG-TERM
LOAD FORECASTING
METHODS
These models are useful for medium and long
term forecasting. The three types of electricity
demand forecasting methods are:
1. Trend Analysis
2. End Use Analysis
3. Econometrics
Each of the three forecasting methods uses a
different approach to determine electricity
demand during a specific year in a particular
place. Each forecasting method is distinctive in
its handling of the four basic forecast
ingredients:
1. The mathematical expressions of the
relationship between electricity demand
and the factors which influence or affect
it - the function
2. The factors which actually influence
electricity demand (population, income,
prices, etc.) - the independent variables
3. Electricity demand itself - the
dependent variable
4. How much electricity demand changes
in response to population, income,
price, etc., changes- the elasticities?
The only way to determine the accuracy of any
load forecast is to wait until the forecast year has
ended and then compare the actual load to the
forecast load. Even though the whole idea of
forecasts is accuracy, nothing was said in the
comparison of the three forecasting methods
about which method produces the most accurate
forecasts. The only thing certain shut any long-
range forecast is that it can never be absolutely
precise. Forecasting accuracy depends on the
quality and quantity of the historical data used,
the validity of the forecasters basic assumptions,
and the accuracy of the forecasts of the demand-
influencing factors (population, income, price,
etc.). None of these is ever perfect.
Consequently, regional load forecasts are
reviewed some are revised yearly. Even so, there
is simply electricity demand will be exactly as
forecast, no is used or who makes the forecast.
Continually, and no assurance that matter what
method is used or who makes the forecast [3].
5.1 Trend Analysis
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
www.IJCSI.org 509
7. Trend analysis (trending) extends past growth
rates of electricity demand into the future, using
techniques that range from hand-drawn straight
lines to complex computer-produced curves.
These extensions constitute the forecast. Trend
analysis focuses on past changes or movements
in electricity demand and uses them to predict
future changes in electricity demand. Usually,
there is not much explanation of why demand
acts as it does, in the past or in the future.
Trending is frequently modified by informed
judgment, wherein utility forecasters modify
their forecasts based on their knowledge of
future developments which might make future
electricity demand behave differently than it has
in the past.
The advantage of trend analysis is that it is
simple, quick and inexpensive to perform. It is
useful when there is not enough data to use more
sophisticated methods or when time and funding
do not allow for a more elaborate approach.
The disadvantage of a trend forecast is that it
produces only one result - future electricity
demand. It does not help analyze why electricity
demand behaves the way it does, and it provides
no means to accurately measure how changes in
energy prices or government policies (for
instance) influence electricity demand. Because
the assumptions used to make the forecast
(informed judgments) are usually not spelled out,
there is often no way to measure the impact of a
change in one of the assumptions. Another
shortcoming of trend analysis is that it relies on
past patterns of electricity demand to project
future patterns of electricity demand. This
simplified view of electrical energy could lead to
inaccurate forecasts in times of change,
especially when new concepts such as
conservation and load management must be
included in the analysis [3].
5.2 End Use Analysis
The basic idea of end-use analysis is that the
demand for electricity depends on what it is used
for (the end-use). For instance, by studying
historical data to find out how much electricity is
used for individual electrical appliances in
homes, then multiplying that number by the
projected number of appliances in each home
and multiplying again by the projected number
of homes, an estimate of how much electricity
will be needed to run all household appliances in
a geographical area during any particular year in
the future can be determined. Using similar
techniques for electricity used in business and
industry, and then adding up the totals for
residential, commercial, and industrial sectors, a
total forecast of electricity demand can be
derived. The advantages of end-use analysis is
that it identifies exactly where electricity goes,
how much is used for each purpose, and the
potential for additional conservation for each
end-use. End-use analysis provides specific
information on how energy requirements can be
reduced over time from conservation measures
such as improved insulation levels, increased use
of storm windows, building code changes, or
improved appliance efficiencies. An end-use
model also breaks down electricity into
residential, commercial and industrial demands.
Such a model can be used to forecast load
changes caused by changes within one sector
(residential, for example) and load changes
resulting indirectly from changes in the other
two sectors. Commercial sector end-use models
currently being developed have the capability of
making energy demand forecasts by end-uses as
specific as type of business and type of building.
This is a major improvement over projecting
only sector-wide energy consumption and using
economic and demographic data for large
geographical areas [1].
The disadvantage of end-use analysis is that
most end-use models assume a constant
relationship between electricity and end-use
(electricity per appliance, or electricity used per
dollar of industrial output). This might hold true
over a few years, but over a 10-or 20-year
period, energy savings technology or energy
prices will undoubtedly change, and the
relationships will not remain constant. End-use
analysis also requires extensive data, since all
relationships between electric load and all the
many end-uses must be calculated as precisely as
possible. Data on the existing stock of energy-
consuming capital (buildings, machinery, etc.) in
many cases is very limited. Also, if the data
needed for end-use analysis is not current, it may
not accurately reflect either present or future
conditions, and this can affect the accuracy of the
forecast. Finally, end-use analysis, without an
econometric component that is explained above,
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8. does not take price changes (elasticity of
demand) in electricity or other competing fuels
into consideration.
Ideally this approach is very accurate. However,
it is sensitive to the amount and quality of end-
use data. For example, in this method the
distribution of equipment age is important for
particular types of appliances. End-use forecast
requires less historical data but more information
about customers and their equipment [1].
5.3 Econometric
Econometrics uses economics, mathematics, and
statistics to forecast electricity demand.
Econometrics is a combination of trend analysis
and end-use analysis, but it does not make the
trend-analyst’s assumption that future electricity
demand can be projected based on past demand.
Moreover, unlike many end-use models,
econometrics can allow for variations in the
relationship between electricity input and end-
use.
Econometrics uses complex mathematical
equations to show past relationships between
electricity demand and the factors which
influence that demand. For instance, an equation
can show how electricity demand in the past
reacted to population growth, price changes, etc.
For each influencing factor, the equation can
show whether the factor caused an increase or
decrease in electricity demand, as well as the size
(in percent) of the increase or decrease. For price
changes, the equation can also show how long it
took consumers to respond to the changes. The
equation is then tested and fine tuned to make
sure that it is as reliable a representation as
possible of the past relationships. Once this is
done, projected values of demand-influencing
factors (population, income, prices) are put into
the equation to make the forecast. A similar
procedure is followed for all of the equations in
the model.
The advantages of econometrics are that it
provides detailed information on future levels of
electricity demand, why future electricity
demand increases or decreases, and how
electricity demand is affected by various factors.
In addition, it provides separate load forecasts
for residential, commercial, and industrial
sectors. Because the econometric model is
defined in terms of a multitude of factors (policy
factors, price factors, end-use factors), it is
flexible and useful for analyzing load growth
under different scenarios.
A disadvantage of econometric forecasting is
that in order for an econometric forecast to be
accurate, the changes in electricity demand
caused by changes in the factors influencing that
demand must remain the same in the forecast
period as in the past. This assumption (which is
called constant elasticities) may be hard to
justify, especially where very large electricity
price changes (as opposed to small, gradual
changes) make consumers more sensitive to
electricity prices [3].
Also, the econometric load forecast can only be
as accurate as the forecasts of factors which
influence demand. Because the future is not
known, projections of very important demand-
influencing factors such as electricity, natural
gas, or oil prices over a 10- or 20-year period
are, at best, educated guesses. Finally) many of
the demand-influencing factors which may be
treated and projected individually in the
mathematical equations could actually depend on
each other, and it is difficult to determine the
nature of these interrelationships. For example,
higher industrial electricity rates may decrease
industrial employment, and projecting both of
them to increase at the same time may be
incorrect. A model which treats projected
industrial electricity rates and industrial
employment separately would not show this fact.
Econometric models work best when forecasting
at national, regional, or state levels. For smaller
geographical areas, meeting the model can be a
problem. This is oddly shaped service areas for
which there demographic data.
6.0 COMPARISON OF
ELECTRICAL LOAD
FORECASTING
TECHNIQUES
In the previous discussion we focus on electrical
load forecasting techniques, most forecasting
methods use statistical techniques or artificial
intelligence algorithms such as regression, neural
networks, fuzzy logic, and expert systems. Two
of the methods named trend analysis, end-use
and econometric approach are broadly used for
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9. medium- and long-term forecasting. A variety of
methods, which include the similar day
approach, various regression models, time series,
neural networks, statistical learning algorithms,
fuzzy logic, and expert systems, have been
developed for short-term forecasting.
The method for short-term forecasting are
similar day approach, various regression models,
time series, neural networks, statistical learning
algorithms, fuzzy logic, and expert systems.
Similar day approach is based on searching
historical data of days of one, two or three years
having the similar characteristics to the day of
forecast. Regression is the one of most widely
used statistical techniques. For electric load
forecasting, regression methods are usually used
to model the relationship of load consumption
and other factors such as weather, day type, and
customer class. There are several regression
models for the next day peak forecasting. Their
models contain deterministic influences such as
holidays, random variables influences such as
average loads, and exogenous influences such as
weather. Time series is a very popular approach
for the electrical load forecasting. Two important
models of time series are ARMA and ARIMA.
ARMA and ARIMA use the time and load as the
only input parameters. Since load generally
depends on the weather and time of the day,
ARIMAX is the most natural tool for load
forecasting among the classical time series
models [2].
The other methods are based on Artificial
intelligence, they are called Intelligent Systems.
In Artificial Neural Network, forecasting is
based on the pattern observed from the past
event and estimates the values for the future.
ANN is well suited to forecasting for two
reasons. First, it has been demonstrated that
ANN are able to approximate numerically any
continuous function to be desired accuracy. In
this case the ANN is seen as multivariate,
nonlinear and nonparametric methods. Secondly,
ANNs are date-driven methods, in the sense that
it is not necessary for the researcher to use
tentative modals and then estimate their
parameters. ANNs are able to automatically map
the relationship between input and output, they
learn this relationship and store this learning into
their parameters. An Expert System is a
computer program, which has the ability to act as
an expert. This means this computer program can
reason, explain, and have its knowledge base
expanded as new information becomes available
to it. The load forecast model is built using the
knowledge about the load forecast domain from
an expert in the field. This knowledge is
represented as facts and rules by using the first
predicate logic to represent the facts and IF-
THEN production rules. This representation is
built in what is called the knowledge base
component of the expert system. The search for
solution or reasoning about the conclusion drawn
by the expert system is performed by the
"Inference Engine" component of the expert
system. For any expert system it has to have the
capability to trace its reasoning if asked by the
user. This facility is built through an explanatory
interface component. Fuzzy logic based on the
usual Boolean logic which is used for digital
circuit design. In case of fuzzy logic, the input is
related to the comparison based on qualities. The
advantage of fuzzy logic is that there is no need
of mathematical models for mapping between
inputs and outputs and also there is no need of
precise or even noise free inputs. Based on the
general rules, properly designed fuzzy logic
systems are very strong for the electrical load
forecasting.
The methods for long- and medium-term
forecasting are trend analysis, end-use and
econometric approach. The advantage of trend
analysis is that it is quick, simple and
inexpensive to perform and does not require
much previous data. The basic idea of the end-
use analysis is that the demand for electricity
depends what it use for (the end-use). The
advantages of end-use analysis is that it
identifies exactly where electricity goes, how
much is used for each purpose, and the potential
for additional conservation for each end-use. The
disadvantage of end-use analysis is that most
end-use models assume a constant relationship
between electricity and end-use (electricity per
appliance, or electricity used per dollar of
industrial output). This might hold true over a
few years, but over a 10-or 20-year period,
energy savings technology or energy prices will
undoubtedly change, and the relationships will
not remain constant. The advantages of
econometrics are that it provides detailed
information on future levels of electricity
demand, why future electricity demand increases
or decreases, and how electricity demand is
affected by various factors. A disadvantage of
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10. econometric forecasting is that in order for an
econometric forecast to be accurate, the changes
in electricity demand caused by changes in the
factors influencing that demand must remain the
same in the forecast period as in the past [5].
7.0 CONCLUSION
Modern load forecasting techniques, such as
expert systems, Artificial Neural Networks
(ANN), fuzzy logic, wavelets, have been
developed recently, showing encouraging results.
Among them, ANN methods are particularly
attractive, as they have the ability to handle the
nonlinear relationships between load and the
factors affecting it directly from historical data.
The trend analysis, end-use modeling and
econometric modeling are the most often used
methods for medium- and long-term load
forecasting. Trend analysis (trending) extends
past growth rates of electricity demand into the
future, using techniques that range from hand-
drawn straight lines to complex computer-
produced curves. Descriptions of appliances used
by customers, the sizes of the houses, the age of
equipment, technology changes, customer
behavior, and population dynamics are usually
included in the statistical and simulation models
based on the so-called end-use approach. In
addition, economic factors such as per capita
incomes, employment levels, and electricity
prices are included in econometric models. These
models are often used in combination with the
end-use approach. Long-term forecasts include
the forecasts on the population changes,
economic development, industrial construction,
and technology development.
REFERENCES
[1] “Computational Intelligence
in Time Series Forecasting
Theory and Engineering
Applications” (Advances in
Industrial Control)
by: Ajoy K. Palit, Dobrivoje
Popovic, Springer, 2005.
[2] Ibrahim Mogharm , Saifur
Rehaman, “Analysis and
Evaluation of Five Short
Term Load Forecasting
Techniques” , IEEE
Transactions on Power
Systems, Vol. 4 No. 4,
October 1989.
[3] H.L.Willis, “Distribution load
forecasting”, IEEE Tutorial
course on power distribution
planning, EHO 361-6-PWR,
1992.
[4] J.V. Ringwood “Intelligent
Forecasting of Electricity
Demand”.
www.forecastingprinciples.com
[5] Andrew P. Douglas, Arthur M.
Breipohl, “Risk Due To Load
Forecast Uncertainty in Short
Term Power System Planning”
IEEE Transactions on Power
Systems, Vol. 13 No. 4,
November, 1998.
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011
ISSN (Online): 1694-0814
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