Machine Learning Algorithms for Power Systems and Renewable Energy Applications
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 10 January 2026
Special Issue Editors
Interests: lightning and its effects on power systems; energy management systems for microgrids and polygenerative plants; controls of microgrids and impact of renewables on the transmission/distribution network
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning algorithms for power systems: distribution networks reconfiguration; photovoltaic power production forecasts; and algorithms to support traditional statistical methods in determining the lightning performance of distribution lines; energy management systems for microgrids and polygenerative plants
Interests: lightning; cable modeling; data science; EMC; grounding and earthing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the introduction of Renewable Energy Sources (RESs) into modern power systems has introduced multiple challenges. Specifically, the intrinsic intermittent generation of RESs may affect grid stability and reliability. Consequently, there is a pressing demand for innovative methodologies to enhance the planning and operation of microgrids and energy production plants. Contextually, electricity markets are experiencing structural evolution, aiming to optimize power systems’ efficiency, offering products able to compensate for the stochasticity caused by the production from RESs.
Within this evolving context, methodologies based on Machine Learning (ML) techniques are gaining momentum for their potential in detecting patterns among input variables, hence resulting useful in applications like, as instance, producing accurate forecasts of RES production, load consumption, market requests and prices, or finding the most suitable grid configuration to mitigate steady-state network violations due to relevant share of RES hosted by the grid.
This Special Issue is dedicated to contributions that explore the application of ML techniques in modern power systems. Contributions are encouraged in, but not restricted to, the following areas:
- Forecasting renewable energy production using advanced ML techniques.
- ML algorithms to implement strategies to counteract the variability from RESs in power systems.
- Surrogate modeling of computationally intensive simulations and routines in power systems using ML.
- Integration of ML, Internet of Things, and big data analysis for energy efficiency enhancement.
- Predictive modeling of electricity prices using ML.
- Bidding strategy optimization in energy markets through ML algorithms.
- Smart control and optimization of Battery Energy Storage Systems via ML.
- Enhancing Energy Management Systems through ML-based optimization tools.
- Improving grid operations (e.g., reconfiguration and fault detection) using ML approaches.
Prof. Dr. Renato Procopio
Dr. Alice La Fata
Dr. Rodolfo Antônio Ribeiro De Moura
Guest Editors
Manuscript Submission Information
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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
- machine learning
- renewable energy sources forecast
- load demand forecast
- market strategies
- network reconfiguration
- computational burden of statistical methods
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