Energy Forecasting in the Era of Smart Urbanization
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G1: Smart Cities and Urban Management".
Deadline for manuscript submissions: 5 March 2026 | Viewed by 65
Special Issue Editors
2. Centre for Research and Technology-Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Interests: data science; machine learning; data mining; artificial intelligence; smart cities
Interests: smart cities; big data and cognitive computing; AI; information systems; electrical engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increased pace of urbanization, combined with rapid advancements in renewable energy and the transition towards electrified vehicles and smart infrastructure, has created numerous challenges in managing modern energy systems. Accurate energy loads and generation forecasting are crucial to ensure reliability and stability in sustainable smart city environments.
In this context, state-of-the-art machine learning, statistical and deep learning approaches, and explainable AI are reshaping forecasting methodologies. The goal of energy forecasting is to predict future trends in energy generation, load, and EV charging distribution using a variety of approaches and procedures. These are essential for informing decision-makers in various fields, such as utilities, business, government, and smart cities. Proactive planning, efficient resource allocation, and grid integration of renewable energy sources are made possible by the capacity to predict patterns of energy generation and demand.
This Special Issue aims to present and discuss the most recent advances in energy forecasting for smart urban environments, bringing together contributions from academia and industry that explore both theoretical and applied aspects.
Topics of interest for publication include, but are not limited to, the following:
- Energy load forecasting in urban environments (electricity, heating, cooling, etc.).
- Renewable energy generation forecasting in smart infrastructure (solar, wind, and hybrid).
- Machine and deep learning modeling for energy time series prediction.
- Explainable AI and interpretable forecasting approaches.
- Foundation models.
- Electric vehicle charging demand prediction.
- Energy market forecasting.
- Non-intrusive load/generation monitoring.
- Benchmarking frameworks and reproducible forecasting studies.
Dr. Aristeidis Mystakidis
Prof. Dr. Christos Tjortjis
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
- energy forecasting
- smart cities
- urban energy systems
- renewable energy forecasting
- load forecasting
- machine learning
- deep learning
- time series forecasting
- explainable AI (XAI)
- foundation models
- electric vehicles (EVs) charging prediction
- microgrids and distributed energy resources forecasting
- energy markets
- sustainability
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