Probabilistic load forecasting using post-processed weather ensemble predictions

Abstract

Probabilistic forecasting of electricity demand (load) facilitates the efficient management and operations of energy systems. Weather is a key determinant of load. However, modelling load using weather is challenging because the relationship cannot be assumed to be linear. Although numerous studies have focussed on load forecasting, the literature on using the uncertainty in weather while estimating the load probability distribution is scarce. In this study, we model load for Great Britain using weather ensemble predictions, for lead times from one to six days ahead. A weather ensemble comprises a range of plausible future scenarios for a weather variable. It has been shown that the ensembles from weather models tend to be biased and underdispersed, which requires that the ensembles are post-processed. Surprisingly, the post-processing of weather ensembles has not yet been employed for probabilistic load forecasting. We post-process ensembles based on: (1) ensemble model output statistics: to correct for bias and dispersion errors by calibrating the ensembles, and (2) ensemble copula coupling: to ensure that ensembles remain physically consistent scenarios after calibration. The proposed approach compares favourably to the case when no weather information, raw weather ensembles or post-processed ensembles without ensemble copula coupling are used during the load modelling.

Keywords:

• Probabilistic forecasting
• electricity demand
• weather predictions
• ensemble model output statistics
• ensemble copula coupling

Acknowledgements

The authors would like to thank two anonymous reviewers for their useful comments. The research detailed in the current paper was based on data from the ECMWF obtained through an academic licence for research purposes.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Although load data for 2018 is available, the corresponding weather data was not complete at the time, and we thus restricted the analysis to the complete data set we could obtain from both sources.

Additional information

Funding

This work was supported by the German Research Foundation (DFG) as part of the Research Training Group GRK 2153: Energy Status Data – Informatics Methods for its Collection, Analysis and Exploitation and under Germany’s Excellence Strategy – EXC number 2064/1 – Project number 390727645.