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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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    9. Morini, Mirko & Pinelli, Michele & Spina, Pier Ruggero & Venturini, Mauro, 2013. "Optimal allocation of thermal, electric and cooling loads among generation technologies in household applications," Applied Energy, Elsevier, vol. 112(C), pages 205-214.
    10. 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.
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    13. 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.
    14. 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.
    15. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
    16. Kofi Afrifa Agyeman & Gyeonggak Kim & Hoonyeon Jo & Seunghyeon Park & Sekyung Han, 2020. "An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads," Energies, MDPI, vol. 13(10), pages 1-20, May.
    17. Kornkamol Laung-Iem & Prapita Thanarak, 2021. "Forecasting of Biodiesel Prices in Thailand using Time Series Decomposition Method for Long Term from 2017 to 2036," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 593-600.
    18. Mirlatifi, A.M. & Egelioglu, F. & Atikol, U., 2015. "An econometric model for annual peak demand for small utilities," Energy, Elsevier, vol. 89(C), pages 35-44.
    19. Higgins, Andrew & Grozev, George & Ren, Zhengen & Garner, Stephen & Walden, Glenn & Taylor, Michelle, 2014. "Modelling future uptake of distributed energy resources under alternative tariff structures," Energy, Elsevier, vol. 74(C), pages 455-463.
    20. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    21. 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.
    22. 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.
    23. 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.
    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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