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Artificial Intelligence (AI) for Energy Systems
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Artificial Intelligence (AI) for Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1947

Special Issue Editor

Dr. Murphy M. Peksen

E-Mail Website
Guest Editor
Chair of Energy Systems, TUM School of Engineering and Design, Technical University of Munich, Boltzmannstr 15, 85748 Garching bei Munich, Germany
Interests: multiphysics; clean energy and digital technologies

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionising green energy systems, finding optimal operation solutions, improved material and component performance, energy efficiency, and enabling smarter decision-making for the progression of different stages of research-based development. In the realm of sustainable energy solutions, AI algorithms process a vast amount of data based on both experimental and numerical methods  to predict patterns and parametric relationships, optimise multiphysics efficiency, and manage resources effectively. Physics-based machine learning techniques enable the predictive optimization of processes, reducing downtime and costs. AI-assisted innovative concepts and systems also facilitate the coupling and effective assessment of integrated clean energy technologies. Additionally, human–machine interactions have the potential to develop and evaluate high-performance materials. These advancements not only safely increase reliability and resilience, but they also pave the way for a more sustainable and environmentally friendly energy future, with smaller carbon footprints and fewer wasted resources. AI’s transformative capabilities are promising for addressing complex challenges and driving innovation across transdisciplinary sectors.

Dr. Murphy M. Peksen
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. 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

  • machine learning
  • AI
  • hydrogen
  • clean energy
  • optimisation
  • HP materials
  • multiphysics

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Published Papers (2 papers)

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Research

25 pages, 2110 KiB  
Article
Deep Learning Forecasting Model for Market Demand of Electric Vehicles
by Ahmed Ihsan Simsek, Erdinç Koç, Beste Desticioglu Tasdemir, Ahmet Aksöz, Muammer Turkoglu and Abdulkadir Sengur
Appl. Sci. 2024, 14(23), 10974; https://doi.org/10.3390/app142310974 - 26 Nov 2024
Viewed by 757
Abstract
The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these [...] Read more.
The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model’s ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Energy Systems)
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14 pages, 3398 KiB  
Article
CFD and Artificial Intelligence-Based Machine Learning Synergy for the Assessment of Syngas-Utilizing Pre-Reformer in r-SOC Technology Advancement
by Murphy M. Peksen
Appl. Sci. 2024, 14(22), 10181; https://doi.org/10.3390/app142210181 - 6 Nov 2024
Viewed by 642
Abstract
This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming [...] Read more.
This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming to enhance the preparation of quality r-SOC-ready fuel, which is an indispensable element for successful operation. Evaluating the intricate thermochemistry of syngas-containing reforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. The developed model forecasts and optimizes reforming processes across diverse fuel compositions, encompassing oxygen-containing syngas blends and controlled feedstock outlet process conditions. Impressively, the model’s predictions align closely with CFD outcomes with an error margin as low as 0.34%, underscoring its accuracy and reliability. This research significantly contributes to a deeper understanding and the qualitative enhancement of preparing high-quality syngas for SOC under improved process conditions. Enabling the early availability of valuable information drives forward sustainable research and ensures the safe, consistent operation assessment of r-SOC. Additionally, this strategic approach substantially reduces the need for resource-intensive experiments. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Energy Systems)
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