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Applied Sciences
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AI Technologies Applied to Energy Systems and Smart Grids
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AI Technologies Applied to Energy Systems and Smart Grids

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 May 2026 | Viewed by 794

Special Issue Editors

Dr. Jacopo Carlo Alberizzi

E-Mail Website
Guest Editor
Faculty of Engineering, Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy
Interests: hybrid renewable energy system technologies; energy system optimization; battery storage systems
Dr. Luciano De Tommasi

E-Mail Website
Guest Editor
IERC-International Energy Research Centre, Cork, Ireland
Interests: energy efficiency; building heating systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy systems and smart grids are essential assets in addressing the urgent challenges posed by climate change and global warming. To mitigate these risks, smart grids must effectively integrate diverse energy system technologies, including renewable sources, while also managing the inherent complexities of the grid. Renewable energy sources, such as solar energy and wind power, are highly variable and unpredictable, which introduces new challenges in balancing supply and demand. Additionally, grid constraints, such as transmission limits and the need for real-time adjustments, make managing energy flow even more complex.

In this context, the design and management of optimized energy systems, along with advanced smart grids and energy storage solutions, become crucial. These systems must be not only competitive in the market but also provide the reliability and robustness typically associated with traditional fossil fuel-based power generation technologies. Energy storage systems play a vital role in stabilizing the grid by storing excess energy during periods of low demand and releasing it when needed.

Artificial intelligence (AI) plays a key role in optimizing these systems by handling the vast number of variables involved, including fluctuating energy production, grid constraints, and real-time demand. AI-driven algorithms are capable of learning from historical data, predicting future trends, and making real-time decisions to balance the grid efficiently. This level of intelligence allows smart grids to adapt dynamically to changing conditions, offering a higher degree of flexibility and reliability than conventional systems. The integration of AI into optimization algorithms thus ensures that energy systems are not only efficient but also resilient, contributing to the broader goal of sustainability. 

Dr. Jacopo Carlo Alberizzi
Dr. Luciano De Tommasi
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. Applied Sciences 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 2400 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

  • artificial intelligence (AI)
  • optimization
  • smart grids
  • renewable energy
  • energy storage

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Published Papers (1 paper)

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Research

17 pages, 1932 KB  
Article
A Hybrid Framework of Gradient-Boosted Dendritic Units and Fully Connected Networks for Short-Term Photovoltaic Power Forecasting
by Kunlun Cai, Xiucheng Wu, Kangliang Zheng, Chufei Nie, Yuantong Yang, Yiqing Li, Yuan Cao and Xilong Sheng
Appl. Sci. 2026, 16(1), 406; https://doi.org/10.3390/app16010406 - 30 Dec 2025
Viewed by 94
Abstract
To ensure reliable and accurate short-term photovoltaic power generation prediction, this study introduces an integrated forecasting framework that combines the gradient boosting paradigm with a dendritic neural structure, termed Gradient Boosting Multi-Bias Dendritic Units Integrated in a Fully Connected Neural Network (GBMDF). The [...] Read more.
To ensure reliable and accurate short-term photovoltaic power generation prediction, this study introduces an integrated forecasting framework that combines the gradient boosting paradigm with a dendritic neural structure, termed Gradient Boosting Multi-Bias Dendritic Units Integrated in a Fully Connected Neural Network (GBMDF). The proposed GBMDF algorithm minimizes prediction deviations by progressively capturing the nonlinear mappings between residual predictions and environmental variables through an iterative error-correction process. Compared with traditional data-driven learning algorithms, GBMDF can comprehensively utilize multiple meteorological inputs while maintaining strong interpretability and analytical transparency. Furthermore, leveraging the flexibility of the GBMDF, the prediction accuracy of existing models is improved through a proposed compensation enhancement technique. Under this mechanism, GBMDF is trained to offset the residual differences in alternative predictors by examining the correlations between the error patterns of alternative predictors and weather attributes. This enhancement method features a simple concept and effective practical performance. Validation experiments confirm that GBMDF not only achieves higher accuracy in photovoltaic output prediction but also improves the overall efficiency of other forecasting methods. Full article
(This article belongs to the Special Issue AI Technologies Applied to Energy Systems and Smart Grids)
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