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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 | Viewed by 212

Special Issue Editors


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Guest Editor
Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Via Opera Pia, 11a, 16145 Genoa, Italy
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
Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genoa, Italy
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

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Guest Editor
Electrical Engineering Department (DEPEL), Federal University of São João del-Rei (UFSJ), São João del-Rei, MG, Brazil
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

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

  • machine learning
  • renewable energy sources forecast
  • load demand forecast
  • market strategies
  • network reconfiguration
  • computational burden of statistical methods

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Published Papers (1 paper)

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Research

36 pages, 1764 KB  
Article
Short-Term Forecasting of Unplanned Power Outages Using Machine Learning Algorithms: A Robust Feature Engineering Strategy Against Multicollinearity and Nonlinearity
by Khathutshelo Steven Sivhugwana and Edmore Ranganai
Energies 2025, 18(18), 4994; https://doi.org/10.3390/en18184994 - 19 Sep 2025
Viewed by 97
Abstract
Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to [...] Read more.
Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to simultaneously quantify these characteristics using a conventional single (linear or nonlinear) model may lead to inaccurate and costly results. To address this, we propose a hybrid RVM-WT-AdaBoostRT-RF framework using power grid data from the Electricity Supply Commission (Eskom) of South Africa. To achieve model interpretability, the least absolute shrinkage and selection operator (LASSO) is first applied to remedy the adverse effects of multicollinearity through regularisation and variable selection. Secondly, a random forest (RF) is used to select the top 10 most influential variables for each season for further analysis. A relevance vector machine (RVM) captures complex nonlinear relationships separately for each season, while the wavelet transform (WT) decomposes residuals generated from RVM into different frequency subseries (with reduced noise). These subseries are predicted with minimal bias using AdaBoost with regression and threshold (AdaBoostRT). Finally, we stack RVM, AdaBoostRT, RF, and residual individual predictions using RF as a meta-model to produce the final forecast with minimal error accumulation and efficiency. The comparative study, based on point forecast metrics, the Diebold-Mariano test, and prediction interval widths, shows that the proposed model outperforms vector autoregressive (VAR), RF, AdaBoostRT, RVM, and Naïve models. The study results can be utilised for optimising resource allocation, effective power grid management, and customer alerts. Full article
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