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Socioeconomic and climatic impacts on long-term electricity demand: A high-resolution approach through machine learning

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  • Huang, Jin
  • Iglesias, Gregorio

Abstract

Reliable long-term electricity demand prediction is essential for strategic energy planning, particularly as nations transition to renewable energy systems. This study investigates the socioeconomic and climatic impacts on Ireland’s long-term electricity demand using a high-resolution machine learning modelling approach. An artificial neural network (ANN) model is presented to forecast hourly electricity demand up to 2060 under various demographic, economic, and climatic scenarios. Applying real-world and publicly accessible datasets, the research examines the causal factors influencing variations in historical electricity demand across multiple temporal scales. The ANN model, optimized through advanced hyperparameter tuning, incorporates key drivers of electricity consumption, including population growth, GDP, temperature fluctuations, and behavioural patterns. Results reveal a persistent annual increase in electricity demand, driven primarily by demographic trends and economic growth, while different climate scenarios illustrate the impact of warming and extreme cold temperatures on demand profiles. The proposed AI-based approach offers researchers, energy planners, and policymakers a simple and robust tool for modelling high-resolution energy systems and supporting the alignment of renewable energy targets with future consumption needs.

Suggested Citation

  • Huang, Jin & Iglesias, Gregorio, 2025. "Socioeconomic and climatic impacts on long-term electricity demand: A high-resolution approach through machine learning," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225028476
    DOI: 10.1016/j.energy.2025.137205
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