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A new hybrid day-ahead peak load forecasting method for Iran’s National Grid

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  • Moazzami, M.
  • Khodabakhshian, A.
  • Hooshmand, R.

Abstract

This paper presents a new hybrid forecasting engine for day-ahead peak load prediction in Iran National Grid (ING). In this forecasting engine the seasonal data bases of the historical peak load demand on the similar days with their weather information given for three cities (Tehran, Tabriz and Ahvaz) have been used. Wavelet decomposition is used to capture low and high frequency components of each data base from original noisy signals. A separate ANN with an iterative training mechanism which is optimized by genetic algorithm is employed for each low and high frequency data base. A day-ahead peak demand is determined with the reconstruction of low and high frequency output components of each ANN. Simulation results show the effectiveness and the superiority of the proposed strategy when compared with other methods for daily peak load demand forecasting in ING and EUNITE test cases.

Suggested Citation

  • Moazzami, M. & Khodabakhshian, A. & Hooshmand, R., 2013. "A new hybrid day-ahead peak load forecasting method for Iran’s National Grid," Applied Energy, Elsevier, vol. 101(C), pages 489-501.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:489-501
    DOI: 10.1016/j.apenergy.2012.06.009
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    1. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    2. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    3. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    4. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
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    12. Andersen, F.M. & Larsen, H.V. & Juul, N. & Gaardestrup, R.B., 2014. "Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West," Applied Energy, Elsevier, vol. 135(C), pages 523-538.
    13. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    14. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    15. 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.
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    17. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    18. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
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    20. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.

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