Forecasting UK electricity and gas demand under RCP scenarios for net-zero energy security using ML
File(s)
Published version
Author(s)
Razeghi-Jahromi, Dorsa
Strbac, Goran
Ameli, Hossein
Type
Journal Article
Abstract
Climate change is altering energy-demand patterns through changing temperatures and heating and cooling requirements. Long-term energy-demand projections are essential for energy security, infrastructure planning, and preparing net-zero energy systems. However, integrated assessments of climate-sensitive electricity and gas demand trajectories in the UK under long-term climate-forcing pathways and net-zero transition assumptions remains limited. To address this gap, this study develops a scenario-based machine-learning framework to jointly project electricity and gas demand in the UK up to 2050 under climate-forcing pathways. CMIP6 daily temperature projections at 0.25° resolution are used to calculate Heating Degree Days and Cooling Degree Days under low-, intermediate-, and high-forcing pathways, labelled RCP2.6, RCP4.5, and RCP8.5. These indicators are used as inputs to Random Forest models for electricity and gas demand. By 2050, electricity demand under RCP8.5 is 4.1% higher than under RCP2.6, while gas demand is 6.8% lower. Under the net-zero adjustment, gas demand declines because of the assumed 75% reduction in gas use, while electricity demand rises as part of displaced gas demand shifts to electricity. Adjusted electricity demand differs by 2-3 TWh between the highest- and lowest-warming pathways, while adjusted gas demand differs by 7-8 TWh. The framework jointly assesses climate-sensitive electricity and gas demand and links these projections to net-zero gas-reduction and electrification assumptions. The results support electricity-capacity and storage planning, electrification strategies, hydrogen infrastructure investment, and decisions on the future role of gas networks in UK energy-security planning under changing climate and transition conditions across Britain.
Date Issued
2026-08-15
Date Acceptance
2026-08-12
Citation
Energies, 2026, 19 (16)
ISSN
1996-1073
Publisher
MDPI AG
Journal / Book Title
Energies
Volume
19
Issue
16
Copyright Statement
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
License URL
Identifier
10.3390/en19163830
Subjects
electricity and gas demand forecasting
climate change impacts
net-zero energy systems
RCP scenarios
Random Forest
electrification
UK energy system
Publication Status
Published
Article Number
ARTN 3830