Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Applications of Machine Learning and Soft Computing in Energy Use...
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Applications of Machine Learning and Soft Computing in Energy Use Forecasting

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

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 18803

Special Issue Editors

Prof. Dr. Elpiniki I. Papageorgiou

E-Mail Website
Guest Editor
Department of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis Campus, GR 41500 Larissa, Greece
Interests: expert systems and knowledge representation; fuzzy cognitive maps, artificial intelligence; modeling and prediction; decision support systems; data mining; machine learning; medical decision making
Special Issues, Collections and Topics in MDPI journals
Dr. Katarzyna Poczęta

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Automatics and Computer Science, Department of Computer Science Applications, Kielce Univeristy of Technology, al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland
Interests: fuzzy cognitive maps; artificial neural networks; machine learning; evolutionary algorithms; soft computing; decision support systems; intelligent data analysis

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present recent developments in the application of soft computing techniques using novel machine learning algorithms to forecast energy use.

Energy load forecasting has been proven to play a significant role in energy planning and the reduction of consumption. Moreover, the increasing economic and ecological energy costs raise the need to develop new techniques and novel tools which could help in better understanding energy use behavior and finding ways to reduce energy consumption. Nonetheless, there is the need to monitor various variables affecting energy consumption and accurately forecast energy use.

The Special Issue is devoted to original contributions in machine learning algorithms and soft computing techniques applied in energy use forecasting. We invite original papers that showcase innovative models and algorithms, deployed case studies, experimental research, or state-of-the-art reviews on this trajectory. Potential topics include but are not limited to supervised, evolutionary or hybrid learning algorithms, deployment of novel applications on energy use forecasting, explainability models for energy forecasting, comparative analysis of various soft computing, and artificial intelligence models, as well as decision support systems.

Submit your paper and select the Journal “Energies” and the Special Issue “Applications of Machine Learning and Soft Computing in Energy Use Forecasting” via: MDPI submission system. Please contact the special issue editor ([email protected]) for any queries. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Prof. Dr. Elpiniki I. Papageorgiou
Dr. Katarzyna Poczęta
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

  • Energy use forecasting
  • Time series prediction
  • Univariate time series
  • Multivariate time series
  • Machine learning
  • Supervised learning
  • Evolutionary optimization
  • Hybrid algorithms
  • Deep learning
  • Fuzzy logic
  • Soft computing
  • Fuzzy cognitive maps
  • Interpretable models
  • Artificial neural networks
  • Recurrent neural networks
  • Expert systems
  • Decision support systems
  • Energy saving

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 4316 KiB  
Article
Edge-Based Short-Term Energy Demand Prediction
by Alexios Lekidis and Elpiniki I. Papageorgiou
Energies 2023, 16(14), 5435; https://doi.org/10.3390/en16145435 - 17 Jul 2023
Cited by 4 | Viewed by 1752
Abstract
The electrical grid is gradually transitioning towards being an interconnected area of the smart grid, where embedded devices operate in an autonomous manner without any human intervention. An important element for this transition is the energy demand prediction, since the needs for energy [...] Read more.
The electrical grid is gradually transitioning towards being an interconnected area of the smart grid, where embedded devices operate in an autonomous manner without any human intervention. An important element for this transition is the energy demand prediction, since the needs for energy have substantially increased due to the introduction of new and heavy consumption sources, such as electric vehicles. Accurate energy demand prediction, especially for short-term durations (i.e., minutes to hours), allows grid operators to produce the substantial amount needed to satisfy the demand–response equilibrium and avoid peak electricity load conditions that may also lead to blackouts in densely populated areas. However, to achieve such an accuracy level, machine learning (ML) models require extensive training with historical measurements, which is usually resource intensive (e.g., in memory and processing power). Hence, deriving accurate predictions for short-term energy demands is challenging due to the absence of external factors such as environmental data from different regions and seasons and categorical values such as bank/bridging holidays in the ML model. Additionally, existing work focuses on ML model execution on Cloud platforms, which usually does not satisfy the real-time requirements of grid operators for short-term energy demand predictions. To address these challenges, this article presents a new method that considers environmental factors and categorical values to build an energy profile for each consumer in a multi-access edge computing (MEC) framework. The method is also based on the Temporal Fusion Transformer (TFT) ML model, which allows it to learn the temporal dependencies of the gathered historical measurements and predict energy demands with satisfying accuracy. The method is applied to a home energy management system testbed containing photovoltaic systems, smart meters, sensors and actuators for detecting environmental factors (i.e., temperature, humidity and radiation) as well as energy storage systems as an additional energy supply source. The MEC framework is deployed in data concentrator devices where the TFT ML model is executed with low resource requirements, ensuring additional security as the data do not leave the location where they are produced. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

16 pages, 3672 KiB  
Article
A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall
by Xiaolei Wang, Xiaoshu Lü, Lauri Vähä-Savo and Katsuyuki Haneda
Energies 2023, 16(10), 4211; https://doi.org/10.3390/en16104211 - 19 May 2023
Viewed by 1273
Abstract
It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy [...] Read more.
It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy efficiency regulations of buildings promote the demand for new construction materials. However, limited by the size and inhomogeneous structure, the thermal conductivity measurement of wall samples becomes a demanding topic. Additionally, we find the thermal parameter values of the samples measured in the laboratory are different from those obtained by theoretical computation. In this paper, a novel signal-transmissive wall is designed to provide the problem solving of signal connectivity in 5G. We further propose a new thermal conductivity predictor based on the Harmony Search (HS) algorithm to estimate the thermal properties of laboratory-made wall samples. The advantages of our approach over the conventional methods are simplicity and robustness, which can be generalized to a wide range of solid samples in the laboratory measurement. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

15 pages, 9109 KiB  
Article
Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
by Junhui Huang and Sakdirat Kaewunruen
Energies 2023, 16(2), 966; https://doi.org/10.3390/en16020966 - 15 Jan 2023
Cited by 15 | Viewed by 2439
Abstract
Most of the Artificial Intelligence (AI) models currently used in energy forecasting are traditional and deterministic. Recently, a novel deep learning paradigm, called ‘transformer’, has been developed, which adopts the mechanism of self-attention. Transformers are designed to better process and predict sequential data [...] Read more.
Most of the Artificial Intelligence (AI) models currently used in energy forecasting are traditional and deterministic. Recently, a novel deep learning paradigm, called ‘transformer’, has been developed, which adopts the mechanism of self-attention. Transformers are designed to better process and predict sequential data sets (i.e., historical time records) as well as to track any relationship in the sequential data. So far, a few transformer-based applications have been established, but no industry-scale application exists to build energy forecasts. Accordingly, this study is the world’s first to establish a transformer-based model to estimate the energy consumption of a real-scale university library and benchmark with a baseline model (Support Vector Regression) SVR. With a large dataset from 1 September 2017 to 13 November 2021 with 30 min granularity, the results using four historical electricity readings to estimate one future reading demonstrate that the SVR (an R2 of 0.92) presents superior performance than the transformer-based model (an R2 of 0.82). Across the sensitivity analysis, the SVR model is more sensitive to the input close to the output. These findings provide new insights into the research area of energy forecasting in either a specific building or a building cluster in a city. The influences of the number of inputs and outputs related to the transformer-based model will be investigated in the future. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

18 pages, 2108 KiB  
Article
Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks
by Katarzyna Poczeta and Elpiniki I. Papageorgiou
Energies 2022, 15(20), 7542; https://doi.org/10.3390/en15207542 - 13 Oct 2022
Cited by 8 | Viewed by 1522
Abstract
The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network [...] Read more.
The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

24 pages, 6770 KiB  
Article
A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling
by Athanasios Anagnostis, Serafeim Moustakidis, Elpiniki Papageorgiou and Dionysis Bochtis
Energies 2022, 15(6), 1959; https://doi.org/10.3390/en15061959 - 8 Mar 2022
Cited by 3 | Viewed by 2573
Abstract
Modelling of thermal energy storage (TES) systems is a complex process that requires the development of sophisticated computational tools for numerical simulation and optimization. Until recently, most modelling approaches relied on analytical methods based on equations of the physical processes that govern TES [...] Read more.
Modelling of thermal energy storage (TES) systems is a complex process that requires the development of sophisticated computational tools for numerical simulation and optimization. Until recently, most modelling approaches relied on analytical methods based on equations of the physical processes that govern TES systems’ operations, producing high-accuracy and interpretable results. The present study tackles the problem of modelling the temperature dynamics of a TES plant by exploring the advantages and limitations of an alternative data-driven approach. A hybrid bimodal LSTM (H2M-LSTM) architecture is proposed to model the temperature dynamics of different TES components, by utilizing multiple temperature readings in both forward and bidirectional fashion for fine-tuning the predictions. Initially, a selection of methods was employed to model the temperature dynamics of individual components of the TES system. Subsequently, a novel cascading modelling framework was realised to provide an integrated holistic modelling solution that takes into account the results of the individual modelling components. The cascading framework was built in a hierarchical structure that considers the interrelationships between the integrated energy components leading to seamless modelling of whole operation as a single system. The performance of the proposed H2M-LSTM was compared against a variety of well-known machine learning algorithms through an extensive experimental analysis. The efficacy of the proposed energy framework was demonstrated in comparison to the modelling performance of the individual components, by utilizing three prediction performance indicators. The findings of the present study offer: (i) insights on the low-error performance of tailor-made LSTM architectures fitting the TES modelling problem, (ii) deeper knowledge of the behaviour of integral energy frameworks operating in fine timescales and (iii) an alternative approach that enables the real-time or semi-real time deployment of TES modelling tools facilitating their use in real-world settings. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

24 pages, 2513 KiB  
Article
An Incentive-Based Implementation of Demand Side Management in Power Systems
by Vasileios M. Laitsos, Dimitrios Bargiotas, Aspassia Daskalopulu, Athanasios Ioannis Arvanitidis and Lefteri H. Tsoukalas
Energies 2021, 14(23), 7994; https://doi.org/10.3390/en14237994 - 30 Nov 2021
Cited by 16 | Viewed by 3009
Abstract
The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the [...] Read more.
The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

14 pages, 1089 KiB  
Article
Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
by Athanasios Ioannis Arvanitidis, Dimitrios Bargiotas, Aspassia Daskalopulu, Vasileios M. Laitsos and Lefteri H. Tsoukalas
Energies 2021, 14(22), 7788; https://doi.org/10.3390/en14227788 - 21 Nov 2021
Cited by 33 | Viewed by 4365
Abstract
The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to [...] Read more.
The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Soft Computing in Energy Use Forecasting)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop