Advanced Load Forecasting Technologies for Power Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".
Deadline for manuscript submissions: 31 December 2026
Special Issue Editors
Interests: Sihwa Tidal Power; Load forecast; Demand Response; Transient stability; Load pattern
Interests: power system operation and planning, particularly in load forecasting and its applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The global energy transition, driven by the urgency of the climate crisis, is reshaping the power generation landscape and altering the energy mix. As renewable energy sources expand rapidly, the need for systematic power system planning and stable operation has become more critical than ever. The accurate forecasting of both demand and generation—including intermittent renewable resources—has emerged as a key enabler for effective power system operation and planning.
This Special Issue aims to present recent advances in energy forecasting methodologies and their practical applications. We invite the submission of high-quality original research articles, review papers, and technical papers that address the challenges of forecasting power demand and generation with nonlinear and uncertain characteristics, leveraging both traditional statistical methods and modern machine learning techniques.
Topics of interest include, but are not limited to, the following:
- All aspects of energy forecasting’
- Very short-term, short-term, medium-term, and long-term load forecasting;
- Very short-term, short-term, medium-term, and long-term renewable energy forecasting;
- Energy consumption forecasting over various time horizons;
- Machine learning-based forecasting approaches;
- Artificial intelligence techniques, including support vector machines (SVM), fuzzy inference systems, and artificial neural networks (ANN);
- Statistical forecasting models;
- Probabilistic and uncertainty-aware forecasting methods;
- Forecasting for regional integrated energy systems;
- Forecasting that incorporates behind-the-meter (BTM) generation;
- Economic impact analysis of forecasting accuracy;
- Development and application of advanced forecasting models.
We look forward to your contributions that advance the state of the art in load and energy forecasting for modern power systems.
Prof. Dr. Kyung-Bin Song
Dr. Young-Min Wi
Dr. Bosung Kwon
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
- load forecasting
- machine learning
- renewable energy
- advanced forecasting model
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