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Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

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  • Maher Selim

    (Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada
    Department of Mathematics, Trent University, Peterborough, ON K9L 0G2, Canada
    National Institute of Standard, Giza 12212, Egypt)

  • Ryan Zhou

    (Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada
    Department of Mathematics, Trent University, Peterborough, ON K9L 0G2, Canada
    School of Computing, Queen’s University, Kingston, ON K9L 3N6, Canada)

  • Wenying Feng

    (Department of Computer Science, Trent University, Peterborough, ON K9L 0G2, Canada
    Department of Mathematics, Trent University, Peterborough, ON K9L 0G2, Canada)

  • Peter Quinsey

    (Lowfoot Inc., Toronto, ON M5A 2B7, Canada)

Abstract

Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids.

Suggested Citation

  • Maher Selim & Ryan Zhou & Wenying Feng & Peter Quinsey, 2021. "Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design," Energies, MDPI, vol. 14(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:1:p:247-:d:475005
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    References listed on IDEAS

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    Cited by:

    1. Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    2. Abhirup Khanna & Anushree Sah & Vadim Bolshev & Michal Jasinski & Alexander Vinogradov & Zbigniew Leonowicz & Marek Jasiński, 2021. "Blockchain: Future of e-Governance in Smart Cities," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    3. Işık, Cem & Kuziboev, Bekhzod & Ongan, Serdar & Saidmamatov, Olimjon & Mirkhoshimova, Mokhirakhon & Rajabov, Alibek, 2024. "The volatility of global energy uncertainty: Renewable alternatives," Energy, Elsevier, vol. 297(C).

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