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Advances in Artificial Intelligence Applications
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Advances in Artificial Intelligence Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 9211

Special Issue Editors

Dr. Tiago Pinto

E-Mail Website
Guest Editor
Department of Engineering, University of Trás-os-Montes and Alto Douro and INESC-TEC, UTAD’s Pole, 5000-801 Vila Real, Portugal
Interests: smart grid; electricity markets; artificial intelligence; machine learning; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zita Vale

E-Mail Website
Guest Editor
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-465 Porto, Portugal
Interests: artificial intelligence; demand response; electric vehicles; electricity markets; power and energy systems; renewable and sustainable energy; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Faria

E-Mail Website
Section Board Member
GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, LASI—Intelligent Systems Associate Laboratory, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: demand response; electricity markets; energy communities; renewable energy integration; real-time simulation; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Decebal Constantin Mocanu

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: deep artificial neural networks; evolutionary computing; optimization; neuroscience
Dr. Elena Mocanu

E-Mail Website
Guest Editor
Department of Computer Science, University of Twente, 7522NH Enschede, The Netherlands
Interests: machine learning; smart grids; artificial intelligence; autonomous systems

Special Issue Information

Dear Colleagues,

Societies are highly dependent on electricity use to ensure safe, reliable, and comfortable living. A continued increase in demand for electricity demand is expected in the future and it is considered a crucial requirement for economic development. Concerns about the impact of electricity use in the environment and about the eventual fuel-based primary source shortage are presently taken as very serious at scientific, economic, and politic levels. These concerns have led to intensive research and to new energy policies envisaging the increased use of renewable energy sources for electricity production and increased energy use efficiency.

In such a dynamic, complex, and competitive environment as the power and energy sector, the use of artificial intelligence is of crucial importance to take full advantage of the opportunities in the field in order to overcome the challenges that are constantly arising.

This Special Issue welcomes novel contributions in the application of artificial intelligence in power and energy systems and other industrial applications.

Dr. Tiago Pinto
Prof. Dr. Zita Vale
Prof. Dr. Pedro Faria
Dr. Decebal Constantin Mocanu
Dr. Elena Mocanu
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

  • Power and energy systems
  • Artificial intelligence
  • Industrial applications

Published Papers (3 papers)

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Research

20 pages, 9690 KiB  
Article
Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems
by Hafiz M. Asfahan, Uzair Sajjad, Muhammad Sultan, Imtiyaz Hussain, Khalid Hamid, Mubasher Ali, Chi-Chuan Wang, Redmond R. Shamshiri and Muhammad Usman Khan
Energies 2021, 14(13), 3946; https://doi.org/10.3390/en14133946 - 01 Jul 2021
Cited by 25 | Viewed by 3293
Abstract
The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling [...] Read more.
The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Toutdb, wout, and Eoutair). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Applications)
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16 pages, 3570 KiB  
Article
Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction
by En-Jui Liu, Yi-Hsuan Hung and Che-Wun Hong
Energies 2021, 14(3), 619; https://doi.org/10.3390/en14030619 - 26 Jan 2021
Cited by 8 | Viewed by 2242
Abstract
As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract [...] Read more.
As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 · 10−4). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Applications)
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15 pages, 625 KiB  
Article
Knowledge-Based Segmentation to Improve Accuracy and Explainability in Non-Technical Losses Detection
by Albert Calvo, Bernat Coma-Puig, Josep Carmona and Marta Arias
Energies 2020, 13(21), 5674; https://doi.org/10.3390/en13215674 - 30 Oct 2020
Cited by 4 | Viewed by 1936
Abstract
Utility companies have a great interest in identifying energy losses. Here, we focus on Non-Technical Losses (NTL), which refer to losses caused by utility theft or meter errors. Typically, utility companies resort to machine learning solutions to automate and optimise the identification of [...] Read more.
Utility companies have a great interest in identifying energy losses. Here, we focus on Non-Technical Losses (NTL), which refer to losses caused by utility theft or meter errors. Typically, utility companies resort to machine learning solutions to automate and optimise the identification of such losses. This paper extends an existing NTL-detection framework: by including knowledge-based NTL segmentation, we have detected some opportunities for improving the accuracy and the explanations provided to the utility company. Our improved models focus on specific types of NTL and therefore, the explanations provided are easier to interpret, allowing stakeholders to make more informed decisions. The improvements and results presented in the article may benefit other industrial frameworks. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Applications)
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