Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting

被引:59
作者
Ardakani, F. J. [1 ]
Ardehali, M. M. [1 ]
机构
[1] Amirkabir Univ Technol, Tehran Polytech, Dept Elect Engn, Energy Res Ctr, Tehran 158254413, Iran
关键词
Demand side management; Electrical energy consumption; Forecasting; Optimization; ANN; ARTIFICIAL NEURAL-NETWORKS; PARTICLE SWARM; OPTIMIZATION; ALGORITHM; CAUSALITY; SYSTEM; ARIMA;
D O I
10.1016/j.enconman.2013.11.019
中图分类号
O414.1 [热力学];
学科分类号
摘要
Worldwide implementation of demand side management (DSM) programs has had positive impacts on electrical energy consumption (EEC) and the examination of their effects on long-term forecasting is warranted. The objective of this study is to investigate the effects of historical DSM data on accuracy of EEC modeling and long-term forecasting. To achieve the objective, optimal artificial neural network (ANN) models based on improved particle swarm optimization (IPSO) and shuffled frog-leaping (SFL) algorithms are developed for EEC forecasting. For long-term EEC modeling and forecasting for the U.S. for 20102030, two historical data types used in conjunction with developed models include (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population for 1967-2009 period. Simulation results from IPSO-ANN and SFL-ANN models show that using socio-economic indicators as input data achieves lower mean absolute percentage error (MAPE) for long-term EEC forecasting, as compared with EEC data. Based on IPSO-ANN, it is found that, for the U.S. EEC long-term forecasting, the addition of DSM data to socio-economic indicators data reduces MAPE by 36% and results in the estimated difference of 3592.8 MBOE (5849.9 TW h) in EEC for 2010-2030. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:745 / 752
页数:8
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