Forecasting Electricity Demand Using AI and Machine Learning

Dear Colleagues,

Against this background, artificial intelligence (AI) and machine learning (ML) techniques have become increasingly prominent in electricity demand forecasting. These approaches enable the representation of complex relationships, the integration of large and heterogeneous data sources, and adaptive learning under evolving customer behaviour across multiple time scales. As a result, AI- and ML-based forecasting methods are now a core element of data-driven power system analysis and operational decision-making.

This Special Issue aims to present recent advances in AI- and ML-based electricity demand forecasting, with a focus on methodological innovation, model evaluation, and real-world application. It seeks original research and submitted articles that address forecasting challenges across a range of temporal and spatial scales, from short-term operational forecasting to medium- and long-term planning. We welcome contributions that demonstrate improvements in forecasting accuracy, robustness, interpretability, or computational performance, as well as studies that examine how AI-based demand forecasting supports power system operation, market design, and the broader energy transition.

Topics of interest include, but are not limited to, the following:

• ML and deep learning methods for electricity demand forecasting;
• Hybrid models combining statistical and AI-based approaches;
• Explainable and interpretable AI approaches for load forecasting;
• Probabilistic and uncertainty-aware demand forecasting;
• Short-, medium-, and long-term load forecasting;
• Real-time and large-scale forecasting applications;
• The use of smart meter data, high-resolution datasets, and large-scale data sources;
• Demand forecasting under high renewable penetration and widespread electrification;
• Feature engineering and data fusion for demand prediction;
• Case studies and comparative assessments of forecasting methodologies.

Prof. Dr. Gareth Taylor
Dr. Daniil Hulak
Prof. Dr. Chun Sing Lai
Guest Editors

Manuscript Submission Information

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