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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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    1. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
    2. Xie, Jingrui & Hong, Tao, 2016. "GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1012-1016.
    3. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    4. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    5. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    6. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    7. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    8. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    9. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    10. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    11. Tao Hong, 2014. "Energy Forecasting: Past, Present, and Future," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 32, pages 43-48, Winter.
    12. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    13. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    14. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
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