A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from Nationa...

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Autores principales:	Adnan Yousaf, Rao Muhammad Asif, Mustafa Shakir, Ateeq Ur Rehman, Fawaz Alassery, Habib Hamam, Omar Cheikhrouhou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	binary genetic algorithm price forecasting energy management system mean absolute percentage error firefly algorithm Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/95e1b74c4d804371a7b8501511cbbe19
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id	oai:doaj.org-article:95e1b74c4d804371a7b8501511cbbe19
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spelling	oai:doaj.org-article:95e1b74c4d804371a7b8501511cbbe192021-11-25T19:03:33ZA Novel Machine Learning-Based Price Forecasting for Energy Management Systems10.3390/su1322126932071-1050https://doaj.org/article/95e1b74c4d804371a7b8501511cbbe192021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12693https://doaj.org/toc/2071-1050Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.Adnan YousafRao Muhammad AsifMustafa ShakirAteeq Ur RehmanFawaz AlasseryHabib HamamOmar CheikhrouhouMDPI AGarticlebinary genetic algorithmprice forecastingenergy management systemmean absolute percentage errorfirefly algorithmEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12693, p 12693 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	binary genetic algorithm price forecasting energy management system mean absolute percentage error firefly algorithm Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	binary genetic algorithm price forecasting energy management system mean absolute percentage error firefly algorithm Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Adnan Yousaf Rao Muhammad Asif Mustafa Shakir Ateeq Ur Rehman Fawaz Alassery Habib Hamam Omar Cheikhrouhou A Novel Machine Learning-Based Price Forecasting for Energy Management Systems
description	Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.
format	article
author	Adnan Yousaf Rao Muhammad Asif Mustafa Shakir Ateeq Ur Rehman Fawaz Alassery Habib Hamam Omar Cheikhrouhou
author_facet	Adnan Yousaf Rao Muhammad Asif Mustafa Shakir Ateeq Ur Rehman Fawaz Alassery Habib Hamam Omar Cheikhrouhou
author_sort	Adnan Yousaf
title	A Novel Machine Learning-Based Price Forecasting for Energy Management Systems
title_short	A Novel Machine Learning-Based Price Forecasting for Energy Management Systems
title_full	A Novel Machine Learning-Based Price Forecasting for Energy Management Systems
title_fullStr	A Novel Machine Learning-Based Price Forecasting for Energy Management Systems
title_full_unstemmed	A Novel Machine Learning-Based Price Forecasting for Energy Management Systems
title_sort	novel machine learning-based price forecasting for energy management systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/95e1b74c4d804371a7b8501511cbbe19
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A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

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