Artificial Intelligence for Energy Forecasting
A Special Issue of Energies (ISSN 1996-1073) belonging to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 20 January 2027 | Viewed by 1020
Editor
Special Issue Information
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
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Keywords
- energy forecasting
- spatiotemporal modeling
- renewable power prediction
- load forecasting
- energy storage forecasting
- multi-energy systems
- uncertainty quantification
- physics-informed learning
- trustworthy artificial intelligence
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