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A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting

Author

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  • Kalhori, M. Rostam Niakan
  • Emami, I. Taheri
  • Fallahi, F.
  • Tabarzadi, M.

Abstract

Reliable long-term hourly load forecasting is imperative for electricity utilities and planners' decision-making, especially in generation-transmission expansion planning. This paper develops a data-driven knowledge-based system for regional hourly load forecasting in the long-term horizon with contributions in both knowledge base and inference engine. In this regard, in the knowledge base, two types of driving factors are taken into account to determine the forecasting model: long-term trend-related features such as macro-economic factors and short-term factors such as temperature. The knowledge base is decomposed into three-step-related parts to deal with these groups' substantial differences in frequency. Firstly, the long-term trends are identified by the tendency variables. Then, the short-term variability is considered by temperature-related variables in addition to day type. Machine learning regression methods, support vector machines, random forest, and artificial neural networks are compared to determine the non-linear relationship of variables in these steps. The results of these steps are combined in the third step. Then, in the inference engine, a new reasoning method based on fuzzy logic is represented to forecast the regional long-term hourly load under uncertain evidence of temperature as an effective non-predictable feature in the long run. To evaluate the performance of the proposed system, a comparison against five systems is conducted. The results show the superiority of this system compared to the other systems for a publicly available dataset (ISO New England electricity market) as well as Iran’s residential, commercial and agricultural electricity load.

Suggested Citation

  • Kalhori, M. Rostam Niakan & Emami, I. Taheri & Fallahi, F. & Tabarzadi, M., 2022. "A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003853
    DOI: 10.1016/j.apenergy.2022.118975
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    References listed on IDEAS

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    1. da Silva, Felipe L.C. & Cyrino Oliveira, Fernando L. & Souza, Reinaldo C., 2019. "A bottom-up bayesian extension for long term electricity consumption forecasting," Energy, Elsevier, vol. 167(C), pages 198-210.
    2. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    3. Veldman, Else & Gibescu, Madeleine & Slootweg, Han (J.G.) & Kling, Wil L., 2013. "Scenario-based modelling of future residential electricity demands and assessing their impact on distribution grids," Energy Policy, Elsevier, vol. 56(C), pages 233-247.
    4. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    5. Seljom, Pernille & Lindberg, Karen Byskov & Tomasgard, Asgeir & Doorman, Gerard & Sartori, Igor, 2017. "The impact of Zero Energy Buildings on the Scandinavian energy system," Energy, Elsevier, vol. 118(C), pages 284-296.
    6. Lindberg, K.B. & Seljom, P. & Madsen, H. & Fischer, D. & Korpås, M., 2019. "Long-term electricity load forecasting: Current and future trends," Utilities Policy, Elsevier, vol. 58(C), pages 102-119.
    7. Boßmann, T. & Staffell, I., 2015. "The shape of future electricity demand: Exploring load curves in 2050s Germany and Britain," Energy, Elsevier, vol. 90(P2), pages 1317-1333.
    8. Tao Hong & Jason Wilson & Jingrui Xie, 2013. "Long term probabilistic load forecasting and normalization with hourly information," HSC Research Reports HSC/13/13, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    9. Andersen, Frits Møller & Baldini, Mattia & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2017. "Households’ hourly electricity consumption and peak demand in Denmark," Applied Energy, Elsevier, vol. 208(C), pages 607-619.
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    Cited by:

    1. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
    2. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    3. Bianca Magalhães & Pedro Bento & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2024. "Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection," Energies, MDPI, vol. 17(8), pages 1-21, April.
    4. Kaiyan Wang & Haodong Du & Jiao Wang & Rong Jia & Zhenyu Zong, 2023. "An Ensemble Deep Learning Model for Provincial Load Forecasting Based on Reduced Dimensional Clustering and Decomposition Strategies," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    5. Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
    6. Monika Zimmermann & Florian Ziel, 2024. "Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting," Papers 2408.00507, arXiv.org, revised Dec 2024.

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