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Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks

Author

Listed:
  • Carla Sahori Seefoo Jarquin

    (Department of Energy, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Alessandro Gandelli

    (Department of Energy, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Francesco Grimaccia

    (Department of Energy, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Marco Mussetta

    (Department of Energy, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

Abstract

Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well.

Suggested Citation

  • Carla Sahori Seefoo Jarquin & Alessandro Gandelli & Francesco Grimaccia & Marco Mussetta, 2023. "Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks," Forecasting, MDPI, vol. 5(2), pages 1-15, April.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:2:p:21-404:d:1122690
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    References listed on IDEAS

    as
    1. Lloyd, James Robert, 2014. "GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes," International Journal of Forecasting, Elsevier, vol. 30(2), pages 369-374.
    2. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    3. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    4. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    5. Venkataramana Veeramsetty & Dongari Rakesh Chandra & Francesco Grimaccia & Marco Mussetta, 2022. "Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks," Forecasting, MDPI, vol. 4(1), pages 1-16, January.
    6. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
    7. Mansoor, Muhammad & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco, 2021. "Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 282-293.
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