relation: https://khub.utp.edu.my/scholars/5917/
title: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
creator: Raza, M.Q.
creator: Khosravi, A.
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.
publisher: Elsevier Ltd
date: 2015
type: Article
type: PeerReviewed
identifier:   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.  ISSN 13640321     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935845022&doi=10.1016%2fj.rser.2015.04.065&partnerID=40&md5=03fec9ba16182ed335c0b155417cdacc
relation: 10.1016/j.rser.2015.04.065
identifier: 10.1016/j.rser.2015.04.065