In electric industry, electricity loads forecasting has become more and more important, because demand quantity is a major determinant in electricity supply strategy. Furthermore, accurate regional loads forecasting is one of principal factors for electric industry to improve the management performance. Recently, time series analysis and statistical methods have been developed for electricity loads forecasting. However, there are two drawbacks in the past forecasting models: (1) conventional statistical methods, such as regression models are unable to deal with the nonlinear relationships well, because of electricity loads are known to be nonlinear; and (2) the rules generated from conventional statistical methods (i.e., ARIMA), and artificial intelligence technologies (i.e., support vector machines (SVM) and artificial neural networks (ANN)) are not easily comprehensive for policy-maker. Based on these reasons above, this paper proposes a new model, which incorporates one step-ahead concept into adaptive-network-based fuzzy inference system (ANFIS) to build a fusion ANFIS model and enhances forecasting for electricity loads by adaptive forecasting equation. The fuzzy if-then rules produced from fusion ANFIS model, which can be understood for human recognition, and the adaptive network in fusion ANFIS model can deal with the nonlinear relationships. This study optimizes the proposed model by adaptive network and adaptive forecasting equation to improve electricity loads forecasting accuracy. To evaluate forecasting performances, six different models are used as comparison models. The experimental results indicate that the proposed model is superior to the listing models in terms of mean absolute percentage errors (MAPE).
Electricity loads ANFIS Adaptive learning Time series One step-ahead method
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Nowak MP, Schultz R, Westphalen A (2005) A stochastic integer programming model for incorporating day-ahead trading of into hydro-thermal unit commitment. Optim Eng 6:163–176
Pai PF (2006) Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads. Energy Convers Manag 47(15–16):2283–2289
Pai PF, Hong WC (2005) Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr Power Syst Res 74:417–425
Pino R, Parreno J, Gomez A, Priore P (2008) Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Eng Appl Artif Intell 21:53–62
Rnberg RN, Misch WR (2002) A two-stage planning model for power scheduling in a hydro-thermal system under uncertainty. Optim Eng 3:355–378
Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proc IFAC symp fuzzy inform, knowledge representation and decision analysis, pp 55–60
Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19:57–70
Ying LC, Pan MC (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49:205–211