Supervisor
Dr. Muhammad Iqbal
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Accurate short-term electricity price forecasting (EPF) is crucial for efficient operation of the UK’s multi-layered power market, impacting generators, traders, the ESO, and policymakers. Prices are highly volatile and non-linear due to renewables, demand fluctuations, and market coupling across Day-Ahead, Intraday, and Balancing Mechanism venues. Traditional statistical models often fail under such dynamics, while machine learning and deep learning approaches—particularly LSTM, GRU, and hybrid architectures—effectively capture temporal dependencies and exogenous drivers. Empirical evidence shows that these models outperform classical baselines, enabling more accurate scheduling, risk management, and financial savings.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Cooke, S.
(2025) Enhancing UK Electricity Price Forecasting Using Deep Learning. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.97