Abstract
We describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second. The competition entails two distinct forecasting aims to maximise the daily evening peak reduction and using as much solar photovoltaic energy as possible. For the latter, we combine a Bayesian (MCMC) linear regression model with an average generation distribution. For the former, we introduce a new error metric that allows even a simple weighted average combined with a simple linear regression model to score very well using the competition performance metric.
Highlights
The Western Power Distribution (WPD) Presumed Open Data (POD) 6MWh battery storage capacity forecasting competition ran from 28 January 2021 through 18 March
Forecasting PV generation in the unpredictable UK weather is clearly challenging and at least in the forecast week here, a simple average over similar times of year outperforms a regression model based on the forecast luminosity
The discharge results show the importance of choosing an appropriate error metric
Summary
Forecasting for Battery Storage: Choosing the Error Metric. EnergiesThe Western Power Distribution (WPD) Presumed Open Data (POD) 6MWh battery storage capacity forecasting competition ran from 28 January 2021 through 18 March2021. Forecasting for Battery Storage: Choosing the Error Metric. The Western Power Distribution (WPD) Presumed Open Data (POD) 6MWh battery storage capacity forecasting competition ran from 28 January 2021 through 18 March. The competition entails prescribing battery charging and discharging profiles for the upcoming week: 2021, 14, 6274. Academic Editors: Hrvoje Pandžić, William Holderbaum and Stephen Haben. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations The competition entails prescribing battery charging and discharging profiles for the upcoming week: 2021, 14, 6274. https://doi.org/
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