Probabilistic forecasting of regional net-load with conditional extremes and gridded NWP

Browell, J. and Fasiolo, M. (2021) Probabilistic forecasting of regional net-load with conditional extremes and gridded NWP. IEEE Transactions on Smart Grid, 12(6), pp. 5011-5019. (doi: 10.1109/TSG.2021.3107159)

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The increasing penetration of embedded renewables makes forecasting net-load, consumption less embedded generation, a significant and growing challenge. Here a framework for producing probabilistic forecasts of net-load is proposed with particular attention given to the tails of predictive distributions, which are required for managing risk associated with low-probability events. Only small volumes of data are available in the tails, by definition, so estimation of predictive models and forecast evaluation requires special attention. We propose a solution based on a best-in-class load forecasting methodology adapted for net-load, and model the tails of predictive distributions with the Generalised Pareto Distribution, allowing its parameters to vary smoothly as functions of covariates. The resulting forecasts are shown to be calibrated and sharper than those produced with unconditional tail distributions. In a use-case inspired evaluation exercise based on reserve setting, the conditional tails are shown to reduce the overall volume of reserve required to manage a given risk. Furthermore, they identify periods of high risk not captured by other methods. The proposed method therefore enables user to both reduce costs and avoid excess risk.

Item Type:Articles
Additional Information:Jethro Browell is supported by EPSRC Fellowship (EP/R023484/1) and a visiting position at the University of Bristol as Heilbronn Visitor in Data Science in February 2020.
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Browell, J., and Fasiolo, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Transactions on Smart Grid
ISSN (Online):1949-3061
Published Online:23 August 2021
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in IEEE Transactions on Smart Grid 12(6): 5011-5019
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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