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Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks
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Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks

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 (20 October 2022) | Viewed by 16348

Special Issue Editors

Prof. Dr. María del Carmen Pegalajar Jiménez

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, ETS de Ingenierías Informática y de Telecomunicación (ETSIIT), Universidad de Granada, 18010 Granada, Andalusia, Spain
Interests: machine learning; pattern recognition; computational intelligence; neural networks; deep learning; evolutionary algorithms; artificial intelligence; applied artificial intelligence; fuzzy logic; energy consumption modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Luis G. Baca Ruiz

E-Mail Website
Guest Editor
Department of Software Engineering, Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
Interests: time series; data mining; artificial neural networks; energy efficiency; energy consumption modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New technology and approaches are continuously and rapidly being introduced and implemented in today's energy systems. Machine Learning techniques play an increasingly important role in smart cities, ordinary buildings and renewable energy systems, amongst many others. As a consequence, assessing and modelling energy expenditure is a key task to improve energy efficiency in these systems. Recently, it has been noted that certain kinds of Machine Learning methods are growing in popularity when it comes to dealing with energy-related data for energy modelling and decision-making processes; these models are Artificial Neural Networks.

Traditionally, energy efficiency has mainly been handled using standard control methods in the energy industry. However, the application of intelligent techniques such as Artificial Neural Networks has led to new and sophisticated solutions for energy efficiency improvement. As a result, it is of paramount importance to develop and implement new intelligent techniques to address this problem.

This Special Issue aims to provide comprehensive coverage of energy efficiency and energy modelling using artificial neural networks. Therefore, we invite authors to contribute papers on innovative artificial intelligence applications for energy modelling, including reviews and case studies.

Prof. Dr. María del Carmen Pegalajar Jiménez
Dr. Luis G. Baca Ruiz
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

  • Artificial Neural Networks
  • Deep Learning
  • Energy Consumption Modelling
  • ANN model optimization applied to Energy Consumption
  • Energy monitoring
  • Energy modeling
  • Machine Learning
  • Energy optimization
  • Energy systems

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Published Papers (5 papers)

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Editorial

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3 pages, 183 KiB  
Editorial
Advances in Energy Efficiency through Neural-Network-Based Models
by L. G. B. Ruiz and M. C. Pegalajar
Energies 2023, 16(5), 2258; https://doi.org/10.3390/en16052258 - 27 Feb 2023
Viewed by 1165
Abstract
Currently, new technologies and approaches are continuously and rapidly being introduced and implemented in energy systems [...] Full article
(This article belongs to the Special Issue Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks)

Research

Jump to: Editorial

14 pages, 3717 KiB  
Article
Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study
by L. Cabezón, L. G. B. Ruiz, D. Criado-Ramón, E. J. Gago and M. C. Pegalajar
Energies 2022, 15(22), 8732; https://doi.org/10.3390/en15228732 - 20 Nov 2022
Cited by 13 | Viewed by 2301
Abstract
Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain [...] Read more.
Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables. Full article
(This article belongs to the Special Issue Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks)
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24 pages, 2204 KiB  
Article
On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios
by Eva Andrés, Manuel Pegalajar Cuéllar and Gabriel Navarro
Energies 2022, 15(16), 6034; https://doi.org/10.3390/en15166034 - 19 Aug 2022
Cited by 6 | Viewed by 3586
Abstract
In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum [...] Read more.
In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned. Full article
(This article belongs to the Special Issue Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks)
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12 pages, 351 KiB  
Article
Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector
by Felipe Leite Coelho da Silva, Kleyton da Costa, Paulo Canas Rodrigues, Rodrigo Salas and Javier Linkolk López-Gonzales
Energies 2022, 15(2), 588; https://doi.org/10.3390/en15020588 - 14 Jan 2022
Cited by 38 | Viewed by 2930
Abstract
Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the [...] Read more.
Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis. Full article
(This article belongs to the Special Issue Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks)
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23 pages, 4780 KiB  
Article
Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles
by Jarosław Ziółkowski, Mateusz Oszczypała, Jerzy Małachowski and Joanna Szkutnik-Rogoż
Energies 2021, 14(9), 2639; https://doi.org/10.3390/en14092639 - 5 May 2021
Cited by 52 | Viewed by 4882
Abstract
This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order [...] Read more.
This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order to create a predictive model on the basis of which fuel consumption of motor vehicles can be determined. A database containing 1750 records, being a set of information on vehicles manufactured in last decade, was used in the process of training the artificial neural networks. The MLP (Multi-Layer Perceptron) 22-10-3 network has been selected from the created neural networks, which was further subjected to an analysis. In order to determine if the predicted values match the real values, the linear Pearson correlation coefficient r and coefficient of determination R2 were used. For the MLP 22-10-3 neural network, the calculated coefficient r was within range 0.93–0.95, while the coefficient of determination R2 assumed a satisfactory value of more than 0.98. Furthermore, a sensitivity analysis of the predictive model was performed, determining the influence of each input variable on prediction accuracy. Then, a neural network with a reduced number of neurons in the input layer (MLP-20-10-3) was built, retaining a quantity of the hidden and output neurons and the activation functions of the individual layers. The MLP 20-10-3 neural network uses similar values of the r and R2 coefficients as the MLP 22-10-3 neural network. For the evaluation of both neural networks, the measures of the ex post prediction errors were used. Depending on the predicted variable, the MAPE errors for the validation sets reached satisfactory values in the range of 5–8% for MLP 22-10-3 and 6–10% for MLP 20-10-3 neural network, respectively. The prediction tool described is intended for the design of passenger cars equipped with internal combustion engines. Full article
(This article belongs to the Special Issue Energy Efficiency and Energy Consumption Modelling Using Artificial Neural Networks)
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