Short-term load forecasting using artificial neural networks and social media data

Boyd, Alexander; Sun, Hongjian; Black, Mary; Jesson, Simon

Abstract

Evolving practices around energy generation, storage and trading within the UK have made it more necessary than ever to provide accurate means of forecasting electricity demand. This paper considers deep neural networks with convolutional and recurrent layers to investigate the inclusion of various data types as inputs to a load forecasting model, by evaluating 24-hour ahead predictions of electricity demand. Using two case studies in Durham, UK, this paper evaluates the benefits of including temporal and meteorological data, and proposes a novel approach to incorporating social media data to a load forecasting model. Performance is assessed using traditional measures of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), whilst also considering the standard deviation across repeats. Results indicate that Twitter data containing the number of tweets matching a specific query is capable of improving forecasting accuracy for large-scale residential loads.

Citation

Boyd, A., Sun, H., Black, M., & Jesson, S. (2021, September). Short-term load forecasting using artificial neural networks and social media data. Presented at CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution, Online Conference

• Presentation Conference Type: Conference Paper (published)
• Conference Name: CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution
• Start Date: Sep 20, 2021
• End Date: Sep 23, 2021
• Acceptance Date: Jul 14, 2021
• Online Publication Date: Jan 25, 2022
• Publication Date: 2021
• Deposit Date: Jul 21, 2021
• Publisher: IET
• DOI: https://doi.org/10.1049/icp.2021.1552
• Public URL: https://durham-repository.worktribe.com/output/1140613