Special Issue "Machine Learning Prediction Models in Energy Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 10 February 2021.

Special Issue Editors

Prof. Annamária R. Várkonyi-Kóczy
Website
Guest Editor
1. Institute of Automation, Óbuda University, Budapest 1034, Hungary; 2.Department of Mathematics and Informatics, J. Selye University, 945 01 Komarno, Slovakia
Interests: machine learning; soft computing techniques; big data analysis; IoT; predictive analytics; hybrid techniques in intelligent measurement; signal and image processing; modeling and diagnostics; fault diagnostics; optimization
Dr. Amir Mosavi
Website SciProfiles
Guest Editor
1. School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
2. Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest 1034, Hungary
Interests: machine learning; deep learning; ensemble and hybrid models; applied mathematics; soft computing; deep reinforcement learning; machine learning for big data; mathematical IT; hydropower modeling; prediction models; time series prediction; climate models; machine learning for remote sensing; hazard models; extreme events; atmospheric models; forecasting models; predictive analytics; Internet of Things

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the latest advancements in prediction models used in energy systems. We invite scientists from around the world to contribute to developing a comprehensive collection of papers on the progressive and high-impact realm of prediction models and diagnostics methods for energy applications. Novel algorithms, new applications, comparative analysis of models, case studies, and state-of-the-art review papers are particularly welcomed.

Very recently, prediction models have been fundamentally revolutionized thanks to affordable computational power, big data technologies, efficient data handling, pre-processing methods, and most importantly, intelligent learning algorithms. Novel machine learning methods, hybrids, ensembles, and deep learning methods integrated with intelligent optimization, various soft computing techniques, and/or advanced statistical methods are rapidly emerging to deliver models with higher accuracy. Today, prediction models are becoming essential in modelling, handling, and diagnosing energy systems with a growing widespread popularity. From energy generation, conversion, distribution, consumption, power, price, loss, load, and demand forecasting to control, diagnosis, failure identification, performance, and maintenance, novel prediction models have shown great progress with promising results.

Prediction models greatly contribute to empowering energy solutions in a broad range of applications. Sustainable and clean energy, smart grids and networks, NetZero, energy selection, energy-saving, emissions estimation/monitoring, fault detection, batteries, buildings, fuels, wind power, smart energy systems, lighting, solar photovoltaic power, heating systems, fuel cells, energy prices, biofuels, power prediction, safety, security, energy theft, performance, production, energy management, Internet of Things (IoT), smart cities, facility maintenance (including mobility and transportation), renewable energies, and nuclear and other energy wastes management are among the challenging applications of prediction models, and are relevant to this Special Issue. As a response to the recent advancements in this domain, the objective of this collection is to present notable methods and applications of prediction models.

Prof. Annamária R. Várkonyi-Kóczy
Dr. Amir Mosavi
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 papers will be 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 1800 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 systems
  • prediction models
  • machine learning
  • deep learning
  • deep reinforcement learning
  • hybrid and ensemble models
  • soft computing
  • big data
  • Internet of Things (IoT)
  • demand prediction
  • consumption prediction
  • load prediction
  • renewable energy production
  • artificial intelligence
  • explainable artificial intelligence (XAI)
  • smart grids
  • cost prediction
  • systems maintenance
  • short-term and long-term prediction models
  • energy performance
  • solar energy prediction
  • wind energy prediction
  • building energy
  • sustainable energy
  • anomaly detection
  • energy saving
  • energy price prediction
  • energy conversion and management
  • energy conversion efficiency
  • energy and climate change
  • smart urban energy systems
  • nature-inspired optimization algorithms

Published Papers (4 papers)

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Research

Open AccessArticle
Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings
Energies 2020, 13(13), 3340; https://doi.org/10.3390/en13133340 - 30 Jun 2020
Abstract
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization [...] Read more.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning. Full article
(This article belongs to the Special Issue Machine Learning Prediction Models in Energy Systems)
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Open AccessArticle
Modeling Nearly Zero Energy Buildings for Sustainable Development in Rural Areas
Energies 2020, 13(10), 2593; https://doi.org/10.3390/en13102593 - 20 May 2020
Abstract
The energy performance of buildings and energy-saving measures have been widely investigated in recent years. However, little attention has been paid to buildings located in rural areas. The aim of this study is to assess the energy performance of two-story residential buildings located [...] Read more.
The energy performance of buildings and energy-saving measures have been widely investigated in recent years. However, little attention has been paid to buildings located in rural areas. The aim of this study is to assess the energy performance of two-story residential buildings located in the mountainous village of Palangan in Iran and to evaluate the impact of multiple parameters, namely building orientation, window-to-wall ratio (WWR), glazing type, shading devices, and insulation, on its energy performance. To attain a nearly zero energy building design in rural areas, the building is equipped with photovoltaic modules. The proposed building design is then economically evaluated to ensure its viability. The findings indicate that an energy saving of 29% can be achieved compared to conventional buildings, and over 22 MWh of electricity can be produced on an annual basis. The payback period is assessed at 21.7 years. However, energy subsidies are projected to be eliminated in the near future, which in turn may reduce the payback period. Full article
(This article belongs to the Special Issue Machine Learning Prediction Models in Energy Systems)
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Open AccessArticle
Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network
Energies 2020, 13(8), 2060; https://doi.org/10.3390/en13082060 - 20 Apr 2020
Cited by 1
Abstract
The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims [...] Read more.
The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs. Full article
(This article belongs to the Special Issue Machine Learning Prediction Models in Energy Systems)
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Open AccessArticle
Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems
Energies 2020, 13(7), 1718; https://doi.org/10.3390/en13071718 - 04 Apr 2020
Cited by 3
Abstract
Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often [...] Read more.
Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210. Full article
(This article belongs to the Special Issue Machine Learning Prediction Models in Energy Systems)
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