Short-Term Electricity Demand Prediction in Great Britain Using Machine Learning
type: CreativeWork
description: In Great Britain, the electricity sector is transforming into a more sophisticated, dispersed, and time-varying form as a result of rapid decarbonisation, the increasing penetration of renewables, and the changing patterns of electrical consumption. The transition from conventional fossil fuels to weather-dependent energy generation from wind and solar technologies has introduced increasing uncertainty in supply and demand, making accurate and timely predictions critical to the reliable and efficient operation of the grid. This dissertation is intended to review the use of advanced machine learning techniques for the prediction of short-term electricity demand in Great Britain with reliance upon publicly available datasets for the period of January 2020 through December 2023. The analyses are based on four comprehensive datasets that can provide an in-depth data quality analysis: NESO Historical Demand, BMRS Fuels Mix, ESPENI Systems Indicators, and Open Meteo Weather Variables, creating a unique dataset comprised of variables that link demand with weather and operation. The study design included creating and validating three different prediction models, LSTM Neural Networks, XGBoost, and Prophet, with a focus on one-and twenty-four-hour ahead electricity demand prediction. Prediction accuracy was measured with traditional statistical metrics (RMSE, MAE) as well as mean absolute percentage error. The results demonstrate that machine learning predictive modelling techniques effectively capture temporal and non-linear dependencies in addition to providing more accurate predictions and providing system operators with practical implications in a developing GB electricity generation system, considering the substantial amount of electrical generation due to renewable resources.
abstract: In Great Britain, the electricity sector is transforming into a more sophisticated, dispersed, and time-varying form as a result of rapid decarbonisation, the increasing penetration of renewables, and the changing patterns of electrical consumption. The transition from conventional fossil fuels to weather-dependent energy generation from wind and solar technologies has introduced increasing uncertainty in supply and demand, making accurate and timely predictions critical to the reliable and efficient operation of the grid. This dissertation is intended to review the use of advanced machine learning techniques for the prediction of short-term electricity demand in Great Britain with reliance upon publicly available datasets for the period of January 2020 through December 2023. The analyses are based on four comprehensive datasets that can provide an in-depth data quality analysis: NESO Historical Demand, BMRS Fuels Mix, ESPENI Systems Indicators, and Open Meteo Weather Variables, creating a unique dataset comprised of variables that link demand with weather and operation. The study design included creating and validating three different prediction models, LSTM Neural Networks, XGBoost, and Prophet, with a focus on one-and twenty-four-hour ahead electricity demand prediction. Prediction accuracy was measured with traditional statistical metrics (RMSE, MAE) as well as mean absolute percentage error. The results demonstrate that machine learning predictive modelling techniques effectively capture temporal and non-linear dependencies in addition to providing more accurate predictions and providing system operators with practical implications in a developing GB electricity generation system, considering the substantial amount of electrical generation due to renewable resources.
author: Tank, Mehul Prafulbhai
keywords: QA76 Meddalwedd Cyfrifiadurol, TJ Perianneg fecanyddol a pheiriannau
date published: 2026-03
date modified: 2026-07-24T05:53:11Z
provider: University of Wales Trinity Saint David
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Wales Trinity Saint David

PropertyValue
property ID: DOI
value: 10.82227/repository.uwtsd.ac.uk.00004225