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A hybrid intelligent algorithm based short-term load forecasting approach

Authors
Journal
International Journal of Electrical Power & Energy Systems
0142-0615
Publisher
Elsevier
Volume
45
Issue
1
Identifiers
DOI: 10.1016/j.ijepes.2012.09.002
Keywords
  • Short-Term Load Forecasting
  • Artificial Neural Network
  • Adaptive Neural
  • Fuzzy Inference System
  • Wavelet Transform
  • Similar-Hour
Disciplines
  • Computer Science
  • Logic

Abstract

Abstract In this paper, a new two-step algorithm is proposed for short-term load forecasting (STLF). In the first step of the method, a wavelet transform (WT) and an artificial neural network (ANN) are used for the primary forecasting of the load over the next 24h. Inputs of this step are weather features (include the daily mean temperature, maximum temperature, mean humidity, and mean wind speed) and previous day load data. In the second step, a WT, the similar-hour method and adaptive neural fuzzy inference system (ANFIS) are used to improve the results of primary load forecasting. In this study, a WT is employed to extract low-order components of the load and weather data. Furthermore, the number of weather data inputs has been reduced by investigating the weather conditions of different cities. To evaluate the performance of the proposed method, it is applied to forecast Iran’s load and New South Wales of Australian’s load. Simulation results in four different cases show that the proposed method increases load forecasting accuracy.

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