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Deep ensemble learning based probabilistic load forecasting in smart grids

Author

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  • Yang, Yandong
  • Hong, Weijun
  • Li, Shufang

Abstract

With the availability of fine-grained smart meter data, there has been increasing interest in using this information for efficient and reliable energy management. In particular, accurate probabilistic load forecasting for individual consumers is critical in determining the uncertainties in future demand with the goal of improving smart grid reliability. Compared with the aggregate loads, individual load profiles exhibit higher irregularity and volatility and thus less predictable. To address these challenges, a novel deep ensemble learning based probabilistic load forecasting framework is proposed to quantify the load uncertainties of individual customers. This framework employs the profiles of different customer groups integrated into the understanding of the task. Specifically, customers are clustered into separate groups based on their profiles and multitask representation learning is employed on these groups simultaneously. This leads to a better feature learning across groups. Case studies conducted on an open access dataset from Ireland demonstrate the effectiveness and superiority of the proposed framework.

Suggested Citation

  • Yang, Yandong & Hong, Weijun & Li, Shufang, 2019. "Deep ensemble learning based probabilistic load forecasting in smart grids," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219320195
    DOI: 10.1016/j.energy.2019.116324
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    References listed on IDEAS

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    14. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
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