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Advances in Artificial Intelligence for Energy Management and Smart Energy System

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: 15 September 2025 | Viewed by 1411

Special Issue Editors

Dr. Panagiotis Michailidis

E-Mail Website
Guest Editor
Center for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece and Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTH), 67100 Xanthi, Greece
Interests: AI for energy systems; predictive control; energy forecasting; smart grids; microgrids; energy efficiency in buildings; energy management systems; adaptive control; internet of things; decarbonization
Dr. Iakovos T. Michailidis

E-Mail Website
Guest Editor
Center for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece and Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTH), 67100 Xanthi, Greece
Interests: energy management optimization; building demand response; renewable energy; energy storage; internet of things; industrial IoT; industrial smart grids; smart grids; smart community grids; smart city grids; microgrids
Special Issues, Collections and Topics in MDPI journals
Dr. Elias B. Kosmatopoulos

E-Mail Website
Guest Editor
Center for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece and Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTH), 67100 Xanthi, Greece
Interests: artificial intelligence; optimal control; algorithms; smart grids; energy efficiency; building energy management; intelligent transportation systems; robotic swarms; internet of things; advanced topics in automatic control systems

Special Issue Information

Dear Colleagues,

Introduction

As we move toward a future with decentralized energy systems, balancing supply and demand with AI-driven algorithms is becoming essential. This Special Issue focuses on how AI can boost energy efficiency, optimize renewable energy resources, enhance demand response, and support smarter decision-making in energy management. AI’s ability to process large volumes of data and make real-time decisions opens exciting possibilities for smarter, more sustainable energy systems. We invite you to submit your research to this Special Issue showcasing the latest advancements in AI for energy management and smart system optimization. Together, we can contribute to building an energy future which is both sustainable and resilient.

Aim

The aim of this Special Issue is to showcase the latest advancements in artificial intelligence that drive innovation in energy management and smart energy systems. The focus is on AI’s role in enhancing energy efficiency, improving grid management, and optimizing the integration of renewable energy resources and energy storage systems. The scope of “Advances in Artificial Intelligence for Energy Management and Smart Energy System” is directly aligned with the mission of MDPI’s journal Energies to advance research in renewable energy, sustainable energy technologies. and the transformation of energy systems. This collection of articles will showcase AI methods tackling energy challenges, focusing on system-wide optimizations in smart grids, including residential and commercial buildings, city grids, renewable energy, EV charging stations, microgrids, industrial grids, and agricultural grids. Our goal is to highlight AI's potential to improve energy distribution, storage, and use across various sectors and scales.

Suggested Themes and Article Types

“Advances in Artificial Intelligence for Energy Management and Smart Energy System” welcomes original research articles and review papers that focus on the application of AI in energy systems. Topics of interest include, but are not limited to, the following:

  • AI optimization for energy use in smart grids and microgrids;
  • AI to boost energy efficiency in residential and commercial buildings;
  • Adaptive control for building energy management systems (BEMSs);
  • Demand response (DR) strategies for building energy management;
  • Machine learning for renewable energy forecasting;
  • Smart control for EV charging stations;
  • AI strategies for EV–grid integration;
  • Adaptive predictive control in energy systems;
  • Reinforcement learning (RL) for demand response;
  • Model predictive control (MPC) for smart grids;
  • Deep learning (DL) for energy system forecasting;
  • Artificial Neural Networks (ANNs) for occupant behavior prediction in buildings;
  • AI in energy storage management at the grid scale;
  • AI’s role in decarbonizing energy systems;
  • IoT and AI for real-time energy monitoring and control.

We look forward to receiving your valuable contributions that will help shape the future of energy systems through artificial intelligence.

Dr. Panagiotis Michailidis
Dr. Iakovos T. Michailidis
Dr. Elias B. Kosmatopoulos
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

  • artificial intelligence
  • energy management optimization
  • smart grids
  • smart homes
  • microgrids
  • smart energy systems
  • building optimization and control
  • building energy management systems
  • renewable energy systems
  • energy storage systems
  • electric vehicles
  • demand response
  • adaptive control
  • reinforcement learning
  • model predictive control
  • internet of things
  • decarbonization

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 6529 KiB  
Article
Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks
by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso and Alejandro Cama-Pinto
Energies 2025, 18(5), 1247; https://doi.org/10.3390/en18051247 - 4 Mar 2025
Cited by 1 | Viewed by 692
Abstract
In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural [...] Read more.
In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10−6 for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model’s accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Energy Management and Smart Energy System)
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19 pages, 2884 KiB  
Article
Detection and Classification of Abnormal Power Load Data by Combining One-Hot Encoding and GAN–Transformer
by Ting Yang, Hongyi Yu, Danhong Lu, Shengkui Bai, Yan Li, Wenyao Fan and Ketian Liu
Energies 2025, 18(5), 1062; https://doi.org/10.3390/en18051062 - 21 Feb 2025
Viewed by 382
Abstract
The explosive growth of power load data has led to a substantial presence of abnormal data, which significantly reduce the accuracy of power system operation planning, load forecasting, and energy usage analysis. To address this issue, a novel improved GAN–Transformer model is proposed, [...] Read more.
The explosive growth of power load data has led to a substantial presence of abnormal data, which significantly reduce the accuracy of power system operation planning, load forecasting, and energy usage analysis. To address this issue, a novel improved GAN–Transformer model is proposed, leveraging the adversarial structure of the generator and discriminator in Generative Adversarial Networks (GANs). To provide the model with a suitable feature dataset, One-hot encoding is introduced to label different categories of abnormal power load data, enabling staged mapping and training of the model with the labeled dataset. Experimental results demonstrate that the proposed model accurately identifies and classifies mutation anomalies, sustained extreme anomalies, spike anomalies, and transient extreme anomalies. Furthermore, it outperforms traditional methods such as LSTM-NDT, Transformer, OmniAnomaly and MAD-GAN in Overall Accuracy, Average Accuracy, and Kappa coefficient, thereby validating the effectiveness and superiority of the proposed anomaly detection and classification method. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Energy Management and Smart Energy System)
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