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Medium-term electric load forecasting using singular value decomposition

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  • Abu-Shikhah, Nazih
  • Elkarmi, Fawwaz

Abstract

Medium-term load forecasting is an important stage in electric power system planning and operation. It is used in maintenance scheduling, and to plan for outages and major works in the power system. A new technique is proposed which uses hourly loads of successive years to predict hourly loads and peak load for the next selected time span. The proposed method implements a new combination of some existing and well established techniques. This is done by first filtering out the load trend, then applying the SVD (singular value decomposition) technique to de-noise the resulting signal. Hourly load is thus divided to three main components: a) a load trend-following component, b) a random component, and c) a de-noised component. Results of applying the technique to the Jordanian power system showed that good forecasting accuracies are attained. In addition, the proposed method outperforms the traditional exponential curve fitting method. The peak load error was found to be less than 5% using the proposed methodology. It was also found that a lag period of 4 years suits the load forecasting purposes of the Jordanian power system. The proposed method is generic and can be implemented to the hourly loads of any power system.

Suggested Citation

  • Abu-Shikhah, Nazih & Elkarmi, Fawwaz, 2011. "Medium-term electric load forecasting using singular value decomposition," Energy, Elsevier, vol. 36(7), pages 4259-4271.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:7:p:4259-4271
    DOI: 10.1016/j.energy.2011.04.017
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    12. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    13. Hu, Zhongyi & Bao, Yukun & Chiong, Raymond & Xiong, Tao, 2015. "Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection," Energy, Elsevier, vol. 84(C), pages 419-431.
    14. Dadkhah, Mojtaba & Jahangoshai Rezaee, Mustafa & Zare Chavoshi, Ahmad, 2018. "Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed," Energy, Elsevier, vol. 148(C), pages 775-788.
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