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Decomposition and statistical analysis for regional electricity demand forecasting

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  • Wang, Chi-hsiang
  • Grozev, George
  • Seo, Seongwon

Abstract

This paper proposes a decomposition approach for modelling the electricity demand trend and variability for medium- and long-term forecasting. This approach decomposes the historical time series into a number of components according to seasonality and day of week. For each component, the yearly and intra-season trends are identified by regression analysis, and the diurnal demand pattern and its associated variability are determined by statistical estimates. Because the decomposition is in line with the changes in seasonality, day of week, and daily activity, the demand models as derived conform to the intuitive interpretation for temporal changes of demand levels. In contrast to most existing methods, this approach does not require involved structural models or time series analysis, saving the efforts of complex non-linear parameter estimations, and is relatively easy for implementation. We apply the proposed approach to half-hourly electricity demand data recorded from 2002 to 2011 for the states of Queensland, Victoria, and the South East Queensland region, Australia. We compare the results for South East Queensland from Monte Carlo simulation with the historical demand, and use it for annual average and peak electricity demand projection up to 2020.

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  • Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    • Handle: RePEc:eee:energy:v:41:y:2012:i:1:p:313-325
    DOI: 10.1016/j.energy.2012.03.011
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    17. 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.
    18. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    19. 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.
    20. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
    21. Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
    22. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
    23. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    24. Yeo, In-Ae & Yoon, Seong-Hwan & Yee, Jurng-Jae, 2013. "Development of an urban energy demand forecasting system to support environmentally friendly urban planning," Applied Energy, Elsevier, vol. 110(C), pages 304-317.

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