Changepoint detection for net electricity demand modelling in Great Britain

Boonsuriyatham, Panthakan (2025) Changepoint detection for net electricity demand modelling in Great Britain. MPhil(R) thesis, University of Glasgow.

Abstract

This thesis investigates a changepoint detection methodology applied to net electricity demand modelling in Great Britain. With the increasing integration of renewable energy sources and the growing complexity of electricity demand patterns, accurately identifying abrupt changes, or “changepoints,” in demand data has become essential for reliable grid management. Initial analyses of national electricity demand data were conducted to explore the underlying features and factors influencing consumption patterns. By integrating Generalised Additive Models (GAMs) within a changepoint detection framework, this research introduces a flexible approach capable of capturing non-linear trends and seasonal variations without requiring extensive manual adjustments, allowing the model to adapt to diverse fluctuations in demand.

Traditional changepoint algorithms were evaluated, but the unique complexities of electricity demand data led to the development of a novel changepoint detection algorithm tailored to these demands. Simulation studies tested the proposed methodology under various mean shifts and noise levels, offering insights into how these parameters impact changepoint detection accuracy. The novel algorithm was subsequently applied to regional Grid Supply Point (GSP) electricity demand data, demonstrating how different demand patterns across geographic areas influence changepoint locations.

The findings underscore both the strengths and limitations of integrating model-based cost functions and GAMs within changepoint detection, particularly in managing daily and seasonal cycles, addressing computational constraints, and scaling across large datasets. This approach enhances the accuracy and flexibility of electricity demand modelling by effectively identifying abrupt changes, enabling a more robust response to demand variability.

• Item Type: Thesis (MPhil(R))
• Qualification Level: Masters
• Subjects: H Social Sciences > HA Statistics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Supervisor's Name: Browell, Professor Jethro and Lee, Professor Duncan Paul
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85614
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 24 Nov 2025 14:33
• Last Modified: 25 Nov 2025 09:43
• Thesis DOI: 10.5525/gla.thesis.85614
• URI: https://theses.gla.ac.uk/id/eprint/85614