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Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm

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  • Bahrami, Saadat
  • Hooshmand, Rahmat-Allah
  • Parastegari, Moein

Abstract

STLF (short term electric load forecasting) plays an important role in the operation of power systems. In this paper, a new model based on combination of the WT (wavelet transform) and GM (grey model) is presented for STLF and is improved by PSO (particle swarm optimization) algorithm. In the proposed model, the weather data including mean temperature, mean relative humidity, mean wind speed, and previous days load data are considered as the model inputs. Also, the wavelet transform is used to eliminate the high frequency components of the previous days load data and improve the accuracy of prediction. To improve the accuracy of STLF, the generation coefficient of GM is enhanced using PSO algorithm. To verify its efficiency, the proposed method is used for New York's and Iran's load forecasting. Simulation results confirm favourable performance of the proposed method in comparison with the previous methods studied.

Suggested Citation

  • Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    • Handle: RePEc:eee:energy:v:72:y:2014:i:c:p:434-442
    DOI: 10.1016/j.energy.2014.05.065
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    References listed on IDEAS

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