A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulat...

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Main Authors: Raza, M.Q., Khosravi, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31396 /
Published: Elsevier Ltd 2015
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935845022&doi=10.1016%2fj.rser.2015.04.065&partnerID=40&md5=03fec9ba16182ed335c0b155417cdacc
http://eprints.utp.edu.my/31396/
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spelling utp-eprints.313962022-03-26T03:18:48Z A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings Raza, M.Q. Khosravi, A. Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings. © 2015 Elsevier Ltd. All rights reserved. Elsevier Ltd 2015 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935845022&doi=10.1016%2fj.rser.2015.04.065&partnerID=40&md5=03fec9ba16182ed335c0b155417cdacc Raza, M.Q. and Khosravi, A. (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50 . pp. 1352-1372. http://eprints.utp.edu.my/31396/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings. © 2015 Elsevier Ltd. All rights reserved.
format Article
author Raza, M.Q.
Khosravi, A.
spellingShingle Raza, M.Q.
Khosravi, A.
A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
author_sort Raza, M.Q.
title A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
title_short A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
title_full A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
title_fullStr A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
title_full_unstemmed A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
title_sort review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
publisher Elsevier Ltd
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935845022&doi=10.1016%2fj.rser.2015.04.065&partnerID=40&md5=03fec9ba16182ed335c0b155417cdacc
http://eprints.utp.edu.my/31396/
_version_ 1741197566482055168
score 11.62408

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