Short-term load forecasting using UK non-domestic businesses to enable demand response aggregators’ participation in electricity markets

Conference item

High-quality short-term load forecasting, particularly day-ahead, is essential to enable the demand response aggregator’s participation in the electricity market. The accuracy of load forecasting depends on many factors, including the size and quality of historical data, selection of the forecasting model, availability of weather data, and types of business sectors. This paper implements three state-of-the-art regression models, ridge regression (RR), random forests (RF), and gradient boosting (GB) to capture intricate variations in three UK cities (Newcastle, Peterborough, and Sheffield) in five business sectors (retail, entertainment, social, industrial, and other) from the UK non-domestic electricity load profiles and provide accurate day-ahead load forecasting. The models are implemented on a historical dataset that contains 7527 UK businesses with geographical postal codes, 30-min electricity consumption, and weather metrics. The performance is evaluated using the coefficient of determination R-squared. The presented results show that GB outperforms RF and RR as it provides the most accurate forecasting results, with limited improvement in forecasting results by including weather data. The aggregated business sectors’ forecasting accuracy is higher than individual business sectors’ forecasts.

Authors: Al Shimmari, M; Wallom, D

• Publication status: Published
• Peer review status: Reviewed (other)
• Publisher: IEEE
• Host title: Grid Edge Technologies Conference & Exposition (Grid Edge), 2023 IEEE PES
• Publication date: 2023-04-18
• Event title: 2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)
• Event series: IEEE PES Grid Edge Technologies Conference & Exposition
• Event location: San Diego, CA, USA
• Event website: https://pes-gridedge.org/
• Event start date: 2023-04-10
• Event end date: 2023-04-13
• DOI: 10.1109/gridedge54130.2023.10102712
• EISBN: 978-1-6654-6012-5
• ISBN: 978-1-6654-6013-2
• Language: English
• Keywords: machine learning (ML); electricity supply industry; smart grids (SG); demand response (DR); demand response; radio frequency; short-term load forecasting (STLF); urban areas; load forecasting; weather forecasting; predictive models; aggregators; FFR