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Artificial Intelligence in Energy Management II
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Artificial Intelligence in Energy Management II

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: closed (30 June 2023) | Viewed by 2602

Special Issue Editor

Prof. Dr. Satoru Okamoto

E-Mail Website
Guest Editor
Interdisciplinary Graduate School of Science and Engineering, Shimane University, Matsue 690-8504, Japan
Interests: advanced thermal and fluids science and technology: flow-induced vibrations; small-scale energy systems with gas turbines and heat pumps; experimental fluid dynamics; heat transfer; biomedical engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defense, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as energies. This Special Issue will provide information on innovation, research, development, and demonstration related to “Artificial Intelligence in Energy Management Systems”. The main focus of this Special Issue is artificial intelligence in conventional and non-conventional thermal energy management systems. Papers are solicited in areas including, but not limited to the following:

  • AI in energy management systems;
  • AI in distributed energy systems;
  • AI in renewable energy systems;
  • AI in energy storage;
  • Demand-side management (DSM) or demand-side response (DSR) by AI;
  • Home energy management system (HEMS) by AI;
  • Building and energy management system (BEMS) by AI;
  • Smart energy management system by AI;
  • Smart building by AI;
  • Smart city by AI;
  • Energy efficiency enhancement by AI;
  • System performance improvement by AI;
  • Energy modeling and simulation;
  • Internet of Things (IoT);
  • Information and communication technology (ICT);
  • Virtual reality (VR), augmented reality (AR), and mixed reality (MR);
  • Big data;
  • 5G;
  • Smart grid;
  • Intelligent control;
  • Artificial intelligence (AI);
  • Machine learning;
  • Deep learning.

Authors are invited to contribute to increasing international cooperation, as well as the understanding and promotion of efforts and disciplines in the area of “Artificial Intelligence in Energy Management Systems”. The dissemination of knowledge by presenting research results, new developments, and novel concepts in “Artificial Intelligence in Energy Management Systems” will serve as the foundation from which this area will be developed.

A variety of topics are available for presentations, allowing authors flexibility.

Prof. Dr. Satoru Okamoto
Guest Editor

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 management system
  • distributed energy system
  • renewable energy system
  • energy storage
  • demand-side management (DSM) or demand-side response (DSR)
  • home energy management system (HEMS)
  • building and energy management system (BEMS)
  • smart energy management system
  • smart building
  • smart city
  • energy efficiency enhancement
  • system performance improvement
  • energy modeling and simulation
  • Internet of Things (IoT)
  • information and communication technology (ICT)
  • virtual reality (VR), augmented reality (AR), and mixed reality (MR)
  • big data
  • 5G
  • smart grid
  • intelligent control
  • artificial intelligence (AI)
  • machine learning
  • deep learning

Published Papers (2 papers)

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Research

18 pages, 3968 KiB  
Article
Comparative Analysis of Data-Driven Algorithms for Building Energy Planning via Federated Learning
by Mazhar Ali, Ankit Kumar Singh, Ajit Kumar, Syed Saqib Ali and Bong Jun Choi
Energies 2023, 16(18), 6517; https://0-doi-org.brum.beds.ac.uk/10.3390/en16186517 - 10 Sep 2023
Cited by 1 | Viewed by 1008
Abstract
Building energy planning is a challenging task in the current mounting climate change scenario because the sector accounts for a reasonable percentage of global end-use energy consumption, with a one-fifth share of global carbon emissions. Energy planners rely on physical model-based prediction tools [...] Read more.
Building energy planning is a challenging task in the current mounting climate change scenario because the sector accounts for a reasonable percentage of global end-use energy consumption, with a one-fifth share of global carbon emissions. Energy planners rely on physical model-based prediction tools to conserve energy and make decisions towards decreasing energy consumption. For precise forecasting, such a model requires the collection of an enormous number of input variables, which is time-consuming because not all the parameters are easily available. Utilities are reluctant to share retrievable consumer information because of growing concerns regarding data leakage and competitive energy markets. Federated learning (FL) provides an effective solution by providing privacy preserving distributed training to relieve the computational burden and security concerns associated with centralized vanilla learning. Therefore, we aimed to comparatively analyze the effectiveness of several data-driven prediction algorithms for learning patterns from data-efficient buildings to predict the hourly consumption of the building sector in centralized and FL setups. The results provided comparable insights for predicting building energy consumption in a distributed setup and for generalizing to diverse clients. Moreover, such research can benefit energy designers by allowing them to use appropriate algorithms via transfer learning on data of similar features and to learn personalized models in meta-learning approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Management II)
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21 pages, 681 KiB  
Article
On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles
by Edoardo Lelli, Alessia Musa, Emilio Batista, Daniela Anna Misul and Giovanni Belingardi
Energies 2023, 16(12), 4639; https://0-doi-org.brum.beds.ac.uk/10.3390/en16124639 - 11 Jun 2023
Cited by 1 | Viewed by 1194
Abstract
The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focuses on establishing [...] Read more.
The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focuses on establishing a correlation between the OCV values, the energy stored in the battery, and the battery SOH. The experimental campaign was conducted at the Hyundai Motor Europe Technical Center GmbH (Germany), and the data collection process took advantage of the ETAS Integrated Calibration and Application Tool (INCA) and the ETAS Measure Data Analyzer (MDA) software. Six machine learning algorithms are evaluated and compared, namely linear regression, k-nearest neighbors, support vector machine, random forest, classification and regression tree, and neural network. Among the evaluated algorithms, random forest (RF) exhibits the best performance in predicting the state of health of high-voltage batteries, both for the OCV and the capacity (C) estimation. Specifically, if compared to the worst algorithm (i.e., linear regression), RF achieves a remarkable improvement with a reduction of 96% and 97% in the mean absolute error for the OCV and the C estimation, respectively. Furthermore, the comparison highlighted the main differences in the performance, complexity, interpretability, and specific features of the six algorithms. The findings of the present study will contribute to the development of efficient maintenance strategies, thus reducing the risk of unexpected battery failures. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Management II)
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