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Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load

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  • Xu, Lei
  • Wang, Shengwei
  • Tang, Rui

Abstract

This paper presents a new probabilistic building load forecasting model considering uncertainties in weather forecasts and abnormal peak load. Two basis function components are developed, including the probabilistic normal load forecasting and the probabilistic uncertain peak load (or abnormal peak load) forecasting. The probabilistic normal load forecasting model is built using the artificial neural network (ANN) and the probabilistic temperature forecasts. The probabilistic abnormal peak load forecasting model consists of two models quantifying the probabilistic occurrence and magnitude of the peak abnormal differential load respectively. The test results show that the ANN deterministic load forecasting model can achieve satisfactory performance with the average mean absolute percentage error (MAPE) of 5.0%. The probabilistic occurrence model can forecast the occurrence frequency of the peak abnormal differential load with the satisfactory agreement, and the probabilistic magnitude model can well forecast the magnitudes of the peak abnormal differential load with the Kolmogorov-Smirnov error of 0.09. Real-time application case studies are conducted by different means of using the probabilistic weather forecasts. The results show that the probabilistic normal load forecasts have satisfactory accuracies and the load forecasts based on the one-day-ahead probabilistic weather forecasts are the best.

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  • Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:180-195
    DOI: 10.1016/j.apenergy.2019.01.022
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    14. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    15. Ullah, Zia & Elkadeem, M.R. & Kotb, Kotb M. & Taha, Ibrahim B.M. & Wang, Shaorong, 2021. "Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply," Renewable Energy, Elsevier, vol. 179(C), pages 885-910.
    16. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
    17. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
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    20. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    21. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
    22. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    23. Zheng, Zhuang & Chen, Hainan & Luo, Xiaowei, 2019. "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, Elsevier, vol. 250(C), pages 882-894.
    24. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).

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