Artificial Intelligence for Energy Forecasting

Dear Colleagues,

Energy forecasting is essential for operating and planning modern energy systems, especially in the context of a  global shift toward renewable energy and increasingly integrated multi-energy systems that has introduced greater variability, non-stationary behavior, and complex spatiotemporal dependencies across various forecasting domains, such as wind power, electrical load, and energy storage. These challenges substantially limit the accuracy and generalization of traditional statistical methods, which commonly rely on simplified stationarity assumptions and struggle to capture strong nonlinearities and long-term spatiotemporal dependencies. Recent progress in artificial intelligence, including graph neural networks, diffusion models, and generative approaches, provides powerful tools for energy forecasting. These methods improve the modeling of nonlinear dynamics, spatiotemporal dependencies, heterogeneous data, and uncertainty, establishing artificial intelligence as a key methodological paradigm in the contemporary energy forecasting domain.

Consequently, this Special Issue aims to present recent advances in artificial intelligence for energy forecasting, particularly focusing on the theoretical and modeling aspects, and its application for modern energy systems.

Areas of interest for publication include, but are not limited to, the following topics:

• Spatiotemporal energy forecasting for complex energy systems;
• AI-based forecasting models for renewable power generation;
• Intelligent load forecasting for demand-side management;
• Power forecasting for energy storage systems;
• AI-driven state estimation for battery systems;
• Energy forecasting methods under high uncertainty and extreme scenarios;
• Physics-informed energy forecasting;
• Trustworthy AI methods for energy forecasting;
• Robust energy forecasting with anomalous and low-quality data;
• Cross-regional and cross-scenario energy forecasting;
• Forecast-informed decision-making and control for energy systems.

Dr. Yun Wang
Guest Editor

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-anonymized 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.