Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load

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Abstract

Home Energy Management Systems (HEMSs) are expected to become an inevitable part of the future smart grid technologies. To work effectively, HEMSs require reliable and accurate load forecasts. In this paper, two new modelling methods are presented. They are both suited for producing multivariate probabilistic forecasts, which consider the temporal correlation between forecast horizons. The first method employs point forecasts generated with Recursive Least Squares (RLS) models and subsequently analyses the forecasts’ residuals to estimate the marginal distributions and temporal correlation. The second method is based on quantile regression to estimate marginal distributions, and a Gaussian copula for linking them together. Furthermore, the application of two modelling approaches for the temporal correlation estimation are investigated for each of the two modelling methods. As a case study, a numerical experiment is designed to emulate an online HEMS operation using data from an inhabited home located in Denmark. Simulation results show a robust performance for the proposed models, with the quantile–copula ensemble outperforming the RLS-based models in predicting the marginal distributions and capturing the temporal correlation.
Original languageEnglish
Article number117594
JournalApplied Energy
Volume303
Number of pages10
ISSN0306-2619
DOIs
Publication statusPublished - 2021

Keywords

  • Multivariate probabilistic forecasting
  • Probabilistic load forecasting
  • Quantile regression
  • Scenario generation
  • Home energy management systems

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