Robust Deep Gaussian Process-Based Probabilistic Electrical Load Forecasting Against Anomalous Events

Di Cao, Weihao Hu, Yingchen Zhang, Qishu Liao, Zhe Chen, Frede Blassbjerg

Research output: Contribution to journalArticlepeer-review

28 Scopus Citations


The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This article proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining the excellent performance of capturing the forecasting uncertainties.
Original languageAmerican English
Pages (from-to)1142-1153
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number2
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5D00-82846


  • anomalous events
  • deep Gaussian process regression
  • limited data
  • probabilistic load forecasting
  • quantification
  • uncertainty


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