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Machine Learning and Data Mining Applications in Power and Multi-Energy Systems

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 40380

Special Issue Editors

Dr. Michał Jasiński

E-Mail Website
Guest Editor
1. Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Interests: power quality; electrical power engineering; renewable energy technologies; machine learning; clean energy; multi-energy systems; data mining; sustainable development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zbigniew Leonowicz

E-Mail Website
Guest Editor
1. Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Interests: signal analysis; advanced signal processing methods; renewable energy; ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is intended as a forum for advancing research and for applying machine learning and data mining in order to facilitate the development of modern electric power systems, grids and devices, smart grids, and protection devices, as well as for developing tools for more accurate and efficient power system analysis and multi-energy systems.

Conventional signal processing is not more adequate for extracting all of the relevant information from distorted signals through filtering, estimation, and detection in order to facilitate decision making and to control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data mining statistical signal detection, and estimation may help in solving contemporary challenges in modern power systems and multi-energy systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; dynamic optimization of grid operations; demand response; incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information and to transform information into actionable intelligence.

The expected outcomes will be a grid with improved situation awareness, faster and more accurate control actions to detect and isolate faults, improved assurance of power quality, and higher levels of energy efficiency.

Dr. Michał Jasiński
Prof. Dr. Zbigniew Leonowicz
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

  • machine learning
  • data mining
  • smart grids
  • power system control
  • power system protection
  • power flow
  • energy management
  • renewable energy
  • demand-side management
  • demand response
  • load scheduling
  • uncertainty estimation
  • power balancing
  • multi-energy systems
  • planning and operation of energy hubs
  • P2X technologies

Published Papers (16 papers)

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Research

Jump to: Review

18 pages, 593 KiB  
Article
Assessing Machine Learning Techniques for Intrusion Detection in Cyber-Physical Systems
by Vinícius F. Santos, Célio Albuquerque, Diego Passos, Silvio E. Quincozes and Daniel Mossé
Energies 2023, 16(16), 6058; https://doi.org/10.3390/en16166058 - 18 Aug 2023
Cited by 3 | Viewed by 1033
Abstract
Cyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation [...] Read more.
Cyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. To answer these questions, this article presents a novel comparison of five offline and three online ML algorithms for intrusion detection using seven CPS-specific datasets, revealing that offline ML is superior when attack signatures are present without time constraints, while online techniques offer a quicker response to new attacks. The findings provide a pathway for enhancing CPS security through a balanced and effective combination of ML techniques. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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17 pages, 1669 KiB  
Article
Analysis of the Behavior Pattern of Energy Consumption through Online Clustering Techniques
by Juan Viera, Jose Aguilar, Maria Rodríguez-Moreno and Carlos Quintero-Gull
Energies 2023, 16(4), 1649; https://doi.org/10.3390/en16041649 - 7 Feb 2023
Cited by 2 | Viewed by 1543
Abstract
Analyzing energy consumption is currently of great interest to define efficient energy management strategies. In particular, studying the evolution of the behavior of the consumption pattern can allow energy policies to be defined according to the time of the year. In this sense, [...] Read more.
Analyzing energy consumption is currently of great interest to define efficient energy management strategies. In particular, studying the evolution of the behavior of the consumption pattern can allow energy policies to be defined according to the time of the year. In this sense, this work proposes to study the evolution of energy behavior patterns using online clustering techniques. In particular, the centroids of the groups constructed by the techniques will represent their consumption patterns. Specifically, two unsupervised online machine learning techniques ideal for the stated objective will be analyzed, X-Means and LAMDA, since they are capable of varying and adapting the number of clusters at runtime. These techniques are applied to energy consumption data in commercial buildings, making groupings on previous groups, in our case, monthly and quarterly. We compared their performance by analyzing the evolution of the patterns over time. The results are very promising since the quality of the consumption patterns obtained is very good according to the performance metrics. Thus, the three main contributions of this article are to propose an approach to determine energy consumption patterns using online non-supervised learning approaches, a methodology to analyze and explain the evolution of energy consumption using centroids of clusters, and a comparison strategy of online learning techniques. The online clustering techniques have qualities of the order of 0.59 and 0.41 for Silhouette and Davies-Boulding, respectively, for X-Means and of the order of 0.71 and 0.24 for Silhouette and Davies-Boulding, respectively, for LAMDA in different datasets of energy. The results are motivating since very good results are obtained in terms of the quality of the clusters, particularly with LAMDA; therefore, analyzing its centroids as the patterns of user behaviors makes a lot of sense. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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16 pages, 4211 KiB  
Article
FDD in Building Systems Based on Generalized Machine Learning Approaches
by William Nelson and Charles Culp
Energies 2023, 16(4), 1637; https://doi.org/10.3390/en16041637 - 7 Feb 2023
Cited by 2 | Viewed by 1395
Abstract
Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process [...] Read more.
Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training. The process can be applied to buildings with a variety of configurations, which saves time and manual effort in a fault analysis. Classification analysis is used for fault detection and diagnostics. An ML analysis is defined which introduces advanced diagnostics with metrics to quantify a fault’s impact in the system and rank detected faults in order of impact severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used. The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a building. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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12 pages, 3819 KiB  
Article
AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System
by Jiaojiao Dong, Mirka Mandich, Yinfeng Zhao, Yang Liu, Shutang You, Yilu Liu and Hongming Zhang
Energies 2023, 16(3), 1401; https://doi.org/10.3390/en16031401 - 31 Jan 2023
Cited by 1 | Viewed by 1325
Abstract
Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a [...] Read more.
Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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21 pages, 7517 KiB  
Article
A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System
by Zahra Jahangiri, Mackenzie Judson, Kwang Moo Yi and Madeleine McPherson
Energies 2023, 16(3), 1352; https://doi.org/10.3390/en16031352 - 27 Jan 2023
Cited by 2 | Viewed by 2012
Abstract
Conventional energy system models have limitations in evaluating complex choices for transitioning to low-carbon energy systems and preventing catastrophic climate change. To address this challenge, we propose a model that allows for the exploration of a broader design space. We develop a supervised [...] Read more.
Conventional energy system models have limitations in evaluating complex choices for transitioning to low-carbon energy systems and preventing catastrophic climate change. To address this challenge, we propose a model that allows for the exploration of a broader design space. We develop a supervised machine learning surrogate of a capacity expansion model, based on residual neural networks, that accurately approximates the model’s outputs while reducing the computation cost by five orders of magnitude. This increased efficiency enables the evaluation of the sensitivity of the outputs to the inputs, providing valuable insights into system development factors for the Canadian electricity system between 2030 and 2050. To facilitate the interpretation and communication of a large number of surrogate model results, we propose an easy-to-interpret method using an unsupervised machine learning technique. Our analysis identified key factors and quantified their relationships, showing that the carbon tax and wind energy capital cost are the most impactful factors on emissions in most provinces, and are 2 to 4 times more impactful than other factors on the development of wind and natural gas generations nationally. Our model generates insights that deepen our understanding of the most impactful decarbonization policy interventions. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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27 pages, 4842 KiB  
Article
Operation of an Energy Storage System Integrated with a Photovoltaic System and an Industrial Customer under Different Real and Pseudo-Real Profiles
by Michał Jasiński, Arsalan Najafi, Tomasz Sikorski, Paweł Kostyła and Jacek Rezmer
Energies 2022, 15(21), 8308; https://doi.org/10.3390/en15218308 - 7 Nov 2022
Viewed by 1319
Abstract
This article presents an idea of the implementation of different real load profiles for energy storage system (ESS) operation. The considered approaches are based on real long-term measurements using energy meters, the adaptation of the standard profiles defined by the distribution system operator [...] Read more.
This article presents an idea of the implementation of different real load profiles for energy storage system (ESS) operation. The considered approaches are based on real long-term measurements using energy meters, the adaptation of the standard profiles defined by the distribution system operator (DSO), as well as a mix of the level of contracted power and short-term measurements. All combinations are used as electricity demand to formulate an ESS operation plan that cooperates with the PV system and the electricity market. The GAMS solver is applied to obtain optimal operation tasks of the ESS to cover different real and pseudo-real load profiles of an industrial company. Obtained results are presented using a real case study of a metallurgy company with a 317 kWp photovoltaic installation and a 200 kW ESS. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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12 pages, 1535 KiB  
Article
Fundamental Studies of Smart Distributed Energy Resources along with Energy Blockchain
by A. J. Jin, C. Li, J. Su and J. Tan
Energies 2022, 15(21), 8067; https://doi.org/10.3390/en15218067 - 30 Oct 2022
Cited by 2 | Viewed by 1494
Abstract
This article studies the broad methodology and major application of smart distributed energy resources (DER) in terms of energy generation, consumption, transaction, and power scheduling. This article simplifies a general DER system into a generic type of integrated DER model. This model is [...] Read more.
This article studies the broad methodology and major application of smart distributed energy resources (DER) in terms of energy generation, consumption, transaction, and power scheduling. This article simplifies a general DER system into a generic type of integrated DER model. This model is used to investigate a smart DER system that transforms three input parameters, (3I parameters) into three critical output functions(3O functions); hence, the model is also called the 3I3O model. The power at a common connection joint can be enabled by a computer that makes computerized decisions to utilize smart DER. Therefore, the computer algorithm collects various data fed into a computer for deep learning and artificial intelligence (AI) decision making. The authors demonstrate important results and the best solutions to meet power demand, offer an economic advantage and have a low carbon footprint for consumers. Moreover, several network blockchain options are discussed. EBC and DER represent an ideal combination with advantages in managing exergy through so-called intelligent power technology. This technology is discussed in detail and includes special hardware, software, and a broad set of computerized intelligence. Finally, the exergy that can possibly be achieved for smart DER systems is discussed. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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15 pages, 626 KiB  
Article
Energy Diversification: A Friend or Foe to Economic Growth in Nordic Countries? A Novel Energy Diversification Approach
by Nihal Ahmed, Adnan Ahmed Sheikh, Farhan Mahboob, Muhammad Sibt e Ali, Elżbieta Jasińska, Michał Jasiński, Zbigniew Leonowicz and Alessandro Burgio
Energies 2022, 15(15), 5422; https://doi.org/10.3390/en15155422 - 27 Jul 2022
Cited by 18 | Viewed by 2943
Abstract
Energy is essential to achieving economic growth, yet the production of energy results in the emission of carbon dioxide, the primary factor in the deterioration of the environment and the acceleration of climate change. In this sense, the diversity of energy sources can [...] Read more.
Energy is essential to achieving economic growth, yet the production of energy results in the emission of carbon dioxide, the primary factor in the deterioration of the environment and the acceleration of climate change. In this sense, the diversity of energy sources can contribute to achieving both environmentally sustainable development. This study investigates the relationship between energy diversification and economic growth in Nordic nations by employing a unique measure of energy diversity. The Nonlinear Panel Autoregressive Distributed Lag (NPARDL) approach is utilized in the research, and it looks at data from 1998 through 2018. According to our results, these nations experience favorable economic growth when there is an increase in the long-term diversity of their energy sources. However, in the near term, they have seen negative economic development due to the diversification of their energy sources. According to these findings, energy diversification benefits Nordic economic growth; however, further research is required for developing economies. As a result, further preventative actions must be implemented while simultaneously diversifying energy sources. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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Review

Jump to: Research

29 pages, 4473 KiB  
Review
An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks
by Sepideh Radhoush, Bradley M. Whitaker and Hashem Nehrir
Energies 2023, 16(16), 5972; https://doi.org/10.3390/en16165972 - 14 Aug 2023
Cited by 2 | Viewed by 1612
Abstract
Distribution grids must be regularly updated to meet the global electricity demand. Some of these updates result in fundamental changes to the structure of the grid network. Some recent changes include two-way communication infrastructure, the rapid development of distributed generations (DGs) in different [...] Read more.
Distribution grids must be regularly updated to meet the global electricity demand. Some of these updates result in fundamental changes to the structure of the grid network. Some recent changes include two-way communication infrastructure, the rapid development of distributed generations (DGs) in different forms, and the installation of smart measurement tools. In addition to other changes, these lead to distribution grid modifications, allowing more advanced features. Even though these advanced technologies enhance distribution grid performance, the operation, management, and control of active distribution networks (ADNs) have become more complicated. For example, distribution system state estimation (DSSE) calculations have been introduced as a tool to estimate the performance of distribution grids. These DSSE computations are highly dependent on data obtained from measurement devices in distribution grids. However, sufficient measurement devices are not available in ADNs due to economic constraints and various configurations of distribution grids. Thus, the modeling of pseudo-measurements using conventional and machine learning techniques from historical information in distribution grids is applied to address the lack of real measurements in ADNs. Different types of measurements (real, pseudo, and virtual measurements), alongside network parameters, are fed into model-based or data-based DSSE approaches to estimate the state variables of the distribution grid. The results obtained through DSSE should be sufficiently accurate for the appropriate management and overall performance evaluation of a distribution grid in a control center. However, distribution grids are prone to different cyberattacks, which can endanger their safe operation. One particular type of cyberattack is known as a false data injection attack (FDIA) on measurement data. Attackers try to inject false data into the measurements of nodes to falsify DSSE results. The FDIA can sometimes bypass poor traditional data-detection processes. If FDIAs cannot be identified successfully, the distribution grid’s performance is degraded significantly. Currently, different machine learning applications are applied widely to model pseudo-measurements, calculate DSSE variables, and identify FDIAs on measurement data to achieve the desired distribution grid operation and performance. In this study, we present a comprehensive review investigating the use of supervised machine learning (SML) in distribution grids to enhance and improve the operation and performance of advanced distribution grids according to three perspectives: (1) pseudo-measurement generation (via short-term load forecasting); (2) DSSE calculation; and (3) FDIA detection on measurement data. This review demonstrates the importance of SML in the management of ADN operation. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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26 pages, 6305 KiB  
Review
Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond
by Ivan S. Maksymov
Energies 2023, 16(14), 5366; https://doi.org/10.3390/en16145366 - 14 Jul 2023
Cited by 6 | Viewed by 1448
Abstract
More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing [...] Read more.
More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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21 pages, 1718 KiB  
Review
Graph-Based Computational Methods for Efficient Management and Energy Conservation in Smart Cities
by Sebastian Ernst, Leszek Kotulski, Adam Sędziwy and Igor Wojnicki
Energies 2023, 16(7), 3252; https://doi.org/10.3390/en16073252 - 5 Apr 2023
Viewed by 1563
Abstract
Computational methods play a significant role in reducing energy consumption in cities. Many different sensor networks (e.g., traffic intensity sensors, intelligent cameras, air quality monitoring systems) generate data that can be useful for both efficient management (including planning) and reducing energy usage. Street [...] Read more.
Computational methods play a significant role in reducing energy consumption in cities. Many different sensor networks (e.g., traffic intensity sensors, intelligent cameras, air quality monitoring systems) generate data that can be useful for both efficient management (including planning) and reducing energy usage. Street lighting is one of the most significant contributors to urban power consumption. This paper presents a summary of recent attempts to use computational methods to reduce energy usage by lighting systems, with special focus on graph-based methods. Such algorithms require all the necessary data to be integrated, in order to function properly: this task is not trivial, and is very time-consuming; therefore, the second part of the paper proposes a novel approach to integrating urban datasets and automating the optimisation process. In two practical examples, we show how spatially triggered graph transformations (STGT) can be used to build a model based on the road network map, sensor locations and street lighting data, and to introduce semantic relations between the objects, including utilisation of existing infrastructure, and planning of development to maximise efficiency. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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23 pages, 6219 KiB  
Review
A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities
by Harleen Kaur Sandhu, Saran Srikanth Bodda and Abhinav Gupta
Energies 2023, 16(6), 2628; https://doi.org/10.3390/en16062628 - 10 Mar 2023
Cited by 7 | Viewed by 3384
Abstract
The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant [...] Read more.
The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali–silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of existing techniques to nuclear systems is somewhat limited because its response requires characterization of high and low-frequency vibration modes, whereas previous studies focus on systems where a single vibration mode can define the degraded state. Data assimilation and storage is another challenging aspect of autonomous control. Advances in AI and data mining world can help to address these challenges. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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21 pages, 27086 KiB  
Review
A Review of Physics-Informed Machine Learning in Fluid Mechanics
by Pushan Sharma, Wai Tong Chung, Bassem Akoush and Matthias Ihme
Energies 2023, 16(5), 2343; https://doi.org/10.3390/en16052343 - 28 Feb 2023
Cited by 22 | Viewed by 11132
Abstract
Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to [...] Read more.
Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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28 pages, 3879 KiB  
Review
Methods and Methodologies for Congestion Alleviation in the DPS: A Comprehensive Review
by Anurag Gautam, Ibraheem, Gulshan Sharma, Mohammad F. Ahmer and Narayanan Krishnan
Energies 2023, 16(4), 1765; https://doi.org/10.3390/en16041765 - 10 Feb 2023
Cited by 2 | Viewed by 2488
Abstract
The modern power system has reached its present state after wading a long path facing several changes in strategies and the implementation of several reforms. Economic and geographical constraints led to reforms and deregulations in the power system to utilize resources optimally within [...] Read more.
The modern power system has reached its present state after wading a long path facing several changes in strategies and the implementation of several reforms. Economic and geographical constraints led to reforms and deregulations in the power system to utilize resources optimally within the existing framework. The major hindrance in the efficient operation of the deregulated power system (DPS) is congestion, which is the result of the participation of private players under deregulation policies. This paper reviews different setbacks introduced by congestion and the methods applied/proposed to mitigate it. Technical and non-technical methods are reviewed and detailed. Major optimization techniques proposed to achieve congestion alleviation are presented comprehensively. This paper combines major publications in the field of congestion management and presents their contribution towards the alleviation of congestion. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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18 pages, 1334 KiB  
Review
A Review of Different Methodologies to Study Occupant Comfort and Energy Consumption
by Antonella Yaacoub, Moez Esseghir and Leila Merghem-Boulahia
Energies 2023, 16(4), 1634; https://doi.org/10.3390/en16041634 - 7 Feb 2023
Cited by 1 | Viewed by 1800
Abstract
The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on [...] Read more.
The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on developing models related to thermal comfort prediction. However, only a few works look at the current state of adaptive thermal comfort studies and the ways in which it could save energy. This study showed that using ML control schemas to make buildings more comfortable in terms of temperature could cut energy by more than 27%. Finally, this paper identifies the remaining difficulties in using ML in thermal comfort investigations, including data collection, thermal comfort indices, sample size, feature selection, model selection, and real-world application. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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26 pages, 1477 KiB  
Review
Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects
by Yu Fujimoto, Akihisa Kaneko, Yutaka Iino, Hideo Ishii and Yasuhiro Hayashi
Energies 2023, 16(3), 1330; https://doi.org/10.3390/en16031330 - 27 Jan 2023
Cited by 7 | Viewed by 2177
Abstract
The widespread introduction of functionally-smart inverters will be an indispensable factor for the large-scale penetration of distributed energy resources (DERs) via the power system. On the other hand, further smartization based on the data-centric operation of smart inverters (S-INVs) is required to cost-effectively [...] Read more.
The widespread introduction of functionally-smart inverters will be an indispensable factor for the large-scale penetration of distributed energy resources (DERs) via the power system. On the other hand, further smartization based on the data-centric operation of smart inverters (S-INVs) is required to cost-effectively achieve the same level of power system operational performance as before under circumstances where the spatio-temporal behavior of power flow is becoming significantly complex due to the penetration of DERs. This review provides an overview of current ambitious efforts toward smartization of operational management of DER inverters, clarifies the expected contribution of machine learning technology to the smart operation of DER inverters, and attempts to identify the issues currently open and areas where research is expected to be promoted in the future. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power and Multi-Energy Systems)
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