Forecasting Electricity Demand Using AI and Machine Learning
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 30 August 2026 | Viewed by 1007
Special Issue Editors
Interests: energy demand forecasting; AI, machine learning; applied mathematics; data communications and information systems
Interests: energy markets; power system operation and management; machine learning; linear programming; AI and mathematical modelling
Interests: AI-driven demand forecasting; electric vehicles; energy and power system optimisation and modelling; energy data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- electricity demand forecasting
- AI
- ML
- deep learning
- short- and long-term load forecasting
- probabilistic demand forecasting
- smart metering
- data-driven power system operation
- energy system balancing
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