%0 Journal Article
%@ 13640321
%A Raza, M.Q.
%A Khosravi, A.
%D 2015
%F scholars:5917
%I Elsevier Ltd
%J Renewable and Sustainable Energy Reviews
%K Distributed power generation; Electric power plant loads; Electric power transmission networks; Energy efficiency; Errors; Expert systems; Forecasting; Genetic algorithms; Hybrid systems; Mean square error; Neural networks; Smart power grids, Abbreviation artificial intelligence artificial intelligence; Adaptation rules; AIS artificial immune system; ANN (artificial neural network); AR auto-regressive; ARIMA auto-regressive integrated moving average; ARMA auto-regressive moving average; Artificial Immune System; Auto-regressive; Autoregressive integrated moving average(ARIMA); Autoregressive/moving averages; BP backpropagation; Correlation coefficient; Demand response; DG distributed generation; DR demand response; ES expert system; GA genetic algorithm; HS hybrid system; Independent system operators; ISO independent system operator; Levenberg-Marquardt; LM levenberg-marquardt; MAE mean absolute error; MAPE mean absolute percent error; Mean absolute error; MLP multi-layer perceptron; MR MADALINE adaptation rule; Multilayers perceptrons; Neural-networks; R correlation coefficient; RMSE root mean square error; Root mean square errors; SB smart building; SG smart grid; Smart grid; Support vectors machine; SVM support vector machine; Times series; TS time series; WNN wavelet neural network, Support vector machines
%P 1352-1372
%R 10.1016/j.rser.2015.04.065
%T A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
%U https://khub.utp.edu.my/scholars/5917/
%V 50
%X 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.
%Z cited By 636