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Artificial Intelligence and Smart Energy

A topical collection in Energies (ISSN 1996-1073). This collection belongs to the section "F5: Artificial Intelligence and Smart Energy".

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Editors


grade E-Mail Website
Collection Editor
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan
Interests: bioenergy; hydrogen energy; clean energy; thermoelectric generation; environmental engineering; AI & machine leaning for energy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Collection Editor
ESADE Business School, Universitat Ramon Llull, Av. Pedralbes 62, Barcelona, Spain
Interests: artificial intelligence; forms of reasoning: qualitative reasoning/fuzzy reasoning; artificial learning; data mining; multi-creiteria and multi-atribute decision-making

E-Mail Website
Collection Editor
The Institute of Computing Technology, Chinese Academy of Sciences, P.O.Box 2704, Beijing 100190, China
Interests: computer architectures and algorithms; parallel and distributed computing; energy efficienct computing

E-Mail Website
Collection Editor
Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
Interests: smart grid; control and planning for microgrid; intelligent methods applied to power systems
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

We are inviting submissions to a Topic Collection of Energies on “Artificial Intelligence and Smart Energy”.

The increase in human growth, alongside a higher standard of living, encourages the community to engage in progressively more activities. This is evident in the massive energy demand. Unfortunately, the current supply does not adequately meet the demands due to some challenges, including costs, techniques, technologies, resources, human skills, etc. To solve these challenges, certain approaches are utilized. However, the traditional practices, which require more resources such as equipment, labor sources, procedures, etc., are tedious and time-consuming. Presently, times are shifting towards the era of digitalization, where all aspects of life are directed towards being fast, effective, and efficient with the assistance of computers.

Artificial intelligence (AI) offers a smart way to help society achieve goals in a modern manner by implementing techniques involving predictive analytics, claims analytics, emerging issues detection, survey analysis, etc. AI covers a wide range, but the fields were not formally founded until 1956, at a conference at Dartmouth College, in Hanover.

On account of the drastic progress in intelligent energy systems, the AI and Smart Energy Topic Collection aims to provide a platform for showcasing the front-line research at the crossing point between AI applications, smart approaches, and energy systems. This Topic Collection also provides the latest research progress in the multidisciplinary approach to AI in energy systems, technology, development, etc. This Topic Collection considers full-length articles, short communications, perspectives, and review articles. Focal points of the AI and Smart Energy Topic Collection include but are not limited to: 

  • Energy topics:
    • Solar thermal energy;
    • Hydropower;
    • Geothermal power;
    • Wind power;
    • Marine energy;
    • Biomass and bioenergy;
    • Hydrogen energy;
    • Nuclear energy;
    • Fossil and green fuels;
    • Energy storage and saving;
    • Energy management;
    • Smart grids;
    • Energy sustainability;
    • Energy modeling.
  • Statistical approaches:
    • Taguchi method;
    • Response surface methodology;
    • Analysis of variance;
    • Linear regression;
    • Others.
  • Artificial intelligence and evolutionary computation:
    • Genetic algorithm;
    • Particle swarm optimization;
    • Nelder–Mead algorithm;
    • Multi-objective genetic algorithm;
    • Others.
  • Data mining and analysis:
    • Neural network;
    • Convolutional neural network;
    • Multivariate adaptive regression splines;
    • Decision tree;
    • K-means clustering;
    • Others.

Prof. Dr. Wei-Hsin Chen
Prof. Dr. Núria Agell
Dr. Zhiyong Liu
Prof. Dr. Ying-Yi Hong
Collection 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 collection 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
  • smart energy
  • intelligent energy system
  • smart approaches
  • AI technology
  • AI development
  • statistical approach
  • evolutionary computation
  • data mining
  • data analysis

Published Papers (7 papers)

2024

Jump to: 2022, 2021

22 pages, 5369 KiB  
Article
Optimal Capacity and Charging Scheduling of Battery Storage through Forecasting of Photovoltaic Power Production and Electric Vehicle Charging Demand with Deep Learning Models
by Fachrizal Aksan, Vishnu Suresh and Przemysław Janik
Energies 2024, 17(11), 2718; https://doi.org/10.3390/en17112718 - 3 Jun 2024
Viewed by 265
Abstract
The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning [...] Read more.
The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure. Full article
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2022

Jump to: 2024, 2021

22 pages, 5941 KiB  
Article
Operation and Multi-Objective Design Optimization of a Plate Heat Exchanger with Zigzag Flow Channel Geometry
by Wei-Hsin Chen, Yi-Wei Li, Min-Hsing Chang, Chih-Che Chueh, Veeramuthu Ashokkumar and Lip Huat Saw
Energies 2022, 15(21), 8205; https://doi.org/10.3390/en15218205 - 3 Nov 2022
Cited by 1 | Viewed by 1986
Abstract
The performance of a plate heat exchanger (PHE) using water as the working fluid with zigzag flow channels was optimized in the present study. The optimal operating conditions of the PHE are explored experimentally by the Taguchi method, with effectiveness as the objective [...] Read more.
The performance of a plate heat exchanger (PHE) using water as the working fluid with zigzag flow channels was optimized in the present study. The optimal operating conditions of the PHE are explored experimentally by the Taguchi method, with effectiveness as the objective function. The results are further verified by the analysis of variance (ANOVA). In addition, the zigzag flow channel geometry is optimized by the non-dominated sorting genetic algorithm-II (NSGA-II), in which the effectiveness and pressure drop of the PHE are considered the two objective functions in the multi-objective optimization process. The experimental results show that the ratio of flow rates is the most important factor affecting the effectiveness of the PHE. The optimal operating conditions are the temperatures of 95 °C and 10 °C at the inlets of hot and cold water flows, respectively, with a cold/hot flow rate ratio of 0.25. The resultant effectiveness is 0.945. Three geometric parameters of the zigzag flow channel are considered, including the entrance length, the bending angle, and the fillet radius. The sensitivity analysis of the parameters reveals that a conflict exists between the two objective functions, and multi-objective optimization is necessary for the zigzag flow channel geometry. The numerical simulations successfully obtain the Pareto optimal front for the two objective functions, which benefits the determination of the geometric design for the zigzag flow channel. Full article
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17 pages, 1021 KiB  
Article
Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches
by Holger Behrends, Dietmar Millinger, Werner Weihs-Sedivy, Anže Javornik, Gerold Roolfs and Stefan Geißendörfer
Energies 2022, 15(2), 582; https://doi.org/10.3390/en15020582 - 14 Jan 2022
Cited by 10 | Viewed by 1964
Abstract
Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults [...] Read more.
Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current. Full article
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2021

Jump to: 2024, 2022

25 pages, 1169 KiB  
Article
Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning
by Henning Schlachter, Stefan Geißendörfer, Karsten von Maydell and Carsten Agert
Energies 2022, 15(1), 104; https://doi.org/10.3390/en15010104 - 23 Dec 2021
Cited by 3 | Viewed by 2692
Abstract
Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This [...] Read more.
Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This is based on the idea to classify loads in the local grid environment of an inverter’s grid connection point to provide information for adaptive control strategies. The proposed concept uses power profiles to systematically generate training data. During hyper-parameter optimizations, multi-layer perceptron (MLP) and convolutional neural networks (CNN) are trained, validated, and evaluated to determine the best task configurations. The approach is demonstrated on the example recognition of two electric vehicles. Finally, the influence of the distance in a test grid from the transformer and the active load to the measurement point, respectively, onto the recognition accuracy is investigated. A larger distance between the inverter and the transformer improved the recognition, while a larger distance between the inverter and active loads decreased the accuracy. The developed concept shows promising results in the simulation environment for adaptive voltage control. Full article
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22 pages, 6400 KiB  
Article
Modeling of Energy Consumption and Reduction of Pollutant Emissions in a Walking Beam Furnace Using the Expert Method—Case Study
by Mariusz Niekurzak and Jerzy Mikulik
Energies 2021, 14(23), 8099; https://doi.org/10.3390/en14238099 - 3 Dec 2021
Cited by 9 | Viewed by 2154
Abstract
This paper presents an algorithm for modeling electricity and natural gas consumption in a walking furnace with the use of artificial intelligence and simulation methods, depending on the length of the rolling campaign and the established rolling program. This algorithm is the basis [...] Read more.
This paper presents an algorithm for modeling electricity and natural gas consumption in a walking furnace with the use of artificial intelligence and simulation methods, depending on the length of the rolling campaign and the established rolling program. This algorithm is the basis for the development of a proposal for a set of minimum requirements characterizing the Best Available Techniques (BAT) for beam furnaces intended for hot rolling, taking into account the requirements set out in national regulations and the recommendations described in the BREF reference documents. This information should be taken into account when drawing up an application for an integrated permit, as well as when setting emission limit values. Based on the constructed algorithm, it was shown that depending on their type and technical specification, the analyzed projects will offer measurable economic benefits in the form of reducing the amount of energy consumed by 1,076,400 kWh during the implementation of 50 rolling campaigns to reduce gas by 14,625 GJ and environmental benefits in the form of reduction of pollutant emissions into the atmosphere 80–360 g/Mg. The constructed algorithm was validated in the Dosimis-3 program, based on a discrete event-driven simulation. Thanks to this representation of the model, its user can interactively participate in changes that take place in the model and thus evaluate its behavior. The model, verified in real conditions, can be the basic source of information for making effective operational technological decisions related to the preparation of production at the rolling mill as part of planning and long-term activities. Full article
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16 pages, 1788 KiB  
Article
Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
by Yose Wandy, Marcus Vogt, Rushit Kansara, Clemens Felsmann and Christoph Herrmann
Energies 2021, 14(21), 7271; https://doi.org/10.3390/en14217271 - 3 Nov 2021
Viewed by 2271
Abstract
The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are [...] Read more.
The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC. Full article
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20 pages, 2660 KiB  
Article
Dynamic Uncertain Causality Graph Applied to the Intelligent Evaluation of a Shale-Gas Sweet Spot
by Quanying Yao, Bo Yang and Qin Zhang
Energies 2021, 14(17), 5228; https://doi.org/10.3390/en14175228 - 24 Aug 2021
Cited by 1 | Viewed by 1611
Abstract
Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, [...] Read more.
Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, a dynamic uncertainty causality graph (DUCG) is applied for the first time to shale-gas sweet-spot evaluation. A graphical modeling scheme is presented to reduce the difficulty in model construction. The evaluation model is based on expert knowledge and does not depend on data. Through rigorous and efficient reasoning, it guarantees exact and efficient diagnostic reasoning in the case of incomplete information. Multiple conditional events and weighted graphs are proposed for specific problems in shale-gas sweet-spot evaluation, which is an extension of the DUCG that defines only one conditional event for different weighted function events and relies only on the experience of a single expert. These solutions make the reasoning process and results more objective, credible, and interpretable. The model is verified with both complete data and incomplete data. The results show that compared with other methods, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising auxiliary tool for shale-gas sweet spot evaluation. Full article
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