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Advanced Forecasting Techniques and Methods for Energy Systems
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Advanced Forecasting Techniques and Methods for Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 6950

Special Issue Editors

Dr. César Berna-Escriche

E-Mail Website
Guest Editor
Department of Statistics, Operational Research and Quality (DEIOAC) & Institute for Energy Engineering (IIE), Universitat Politècnica de València (UPV), Camino de Vera 14, 46022 Valencia, Spain
Interests: e-learning; data science; educational data mining; educational technology; engineering education; energy engineering; energy generation; renewable energy; energy efficiency
Special Issues, Collections and Topics in MDPI journals
Dr. Paula Bastida-Molina

E-Mail Website
Guest Editor
Institute of Energy Engineering, Polytechnic University of Valencia, 46022 Valencia, Spain
Interests: solar photovoltaic systems; electric vehicles; hybrid renewable energy systems; sustainable energy transition; natural based solutions modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transition towards environmentally friendly power systems is driving an increase in the production of clean energy. Distributed energy resources are increasingly vital in modern power and energy systems, offering benefits such as reduced emissions and enhanced security. However, their variability and uncertainty demand greater flexibility in future energy systems. Consequently, prediction plays a crucial role in asset and resource management across various fields, including energy commodities. Given the stochastic nature of supply and demand, effective scheduling and control require the performance of forecasting. This Special Issue aims to showcase forecasting techniques, emphasizing machine learning and artificial intelligence alongside statistical forecasting techniques and hybrid methodologies. Papers that focus on the development and applications of different analysis tools, from microgrids to continental scale systems, and focus on the planning and management of renewable energy systems, energy portfolios, markets and natural resources are welcome. Original research and review articles that contribute to advanced forecasting and optimization in energy systems are welcome.

Dr. César Berna-Escriche
Dr. Paula Bastida-Molina
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. Applied Sciences 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 2400 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

  • forecasting methods
  • optimization techniques
  • modelling
  • statistical analysis
  • artificial intelligence
  • machine learning
  • renewable resources
  • meteorological data analysis
  • demand-side management
  • electric vehicle

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Published Papers (5 papers)

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Research

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10 pages, 2460 KiB  
Article
Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations
by Keunsoo Ko and Changgyun Kim
Appl. Sci. 2024, 14(22), 10332; https://doi.org/10.3390/app142210332 - 10 Nov 2024
Viewed by 663
Abstract
During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation [...] Read more.
During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of R2 when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs. Full article
(This article belongs to the Special Issue Advanced Forecasting Techniques and Methods for Energy Systems)
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28 pages, 8909 KiB  
Article
A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection
by Joel Torres-Cabrera, Jorge Maldonado-Correa, Marcelo Valdiviezo-Condolo, Estefanía Artigao, Sergio Martín-Martínez and Emilio Gómez-Lázaro
Appl. Sci. 2024, 14(17), 7458; https://doi.org/10.3390/app14177458 - 23 Aug 2024
Viewed by 1039
Abstract
The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address [...] Read more.
The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs. Full article
(This article belongs to the Special Issue Advanced Forecasting Techniques and Methods for Energy Systems)
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21 pages, 5233 KiB  
Article
Three Novel Artificial Neural Network Architectures Based on Convolutional Neural Networks for the Spatio-Temporal Processing of Solar Forecasting Data
by Llinet Benavides Cesar, Miguel-Ángel Manso-Callejo and Calimanut-Ionut Cira
Appl. Sci. 2024, 14(13), 5955; https://doi.org/10.3390/app14135955 - 8 Jul 2024
Viewed by 1308
Abstract
In this work, three new convolutional neural network models—spatio-temporal convolutional neural network versions 1 and 2 (ST_CNN_v1 and ST_CNN_v2), and the spatio-temporal dilated convolutional neural network (ST_Dilated_CNN)—are proposed for solar forecasting and processing global horizontal irradiance (GHI) data enriched with meteorological and astronomical [...] Read more.
In this work, three new convolutional neural network models—spatio-temporal convolutional neural network versions 1 and 2 (ST_CNN_v1 and ST_CNN_v2), and the spatio-temporal dilated convolutional neural network (ST_Dilated_CNN)—are proposed for solar forecasting and processing global horizontal irradiance (GHI) data enriched with meteorological and astronomical variables. A comparative analysis of the proposed models with two traditional benchmark models shows that the proposed ST_Dilated_CNN model outperforms the rest in capturing long-range dependencies, achieving a mean absolute error of 31.12 W/m2, a mean squared error of 54.07 W/m2, and a forecast skill of 37.21%. The statistical analysis carried out on the test set suggested highly significant differences in performance (p-values lower than 0.001 for all metrics in all the considered scenarios), with the model with the lowest variability in performance being ST_CNN_v2. The statistical tests applied confirmed the robustness and reliability of the proposed models under different conditions. In addition, this work highlights the significant influence of astronomical variables on prediction performance. The study also highlights the intricate relationship between the proposed models and meteorological and astronomical input characteristics, providing important insights into the field of solar prediction and reaffirming the need for further research into variability factors that affect the performance of models. Full article
(This article belongs to the Special Issue Advanced Forecasting Techniques and Methods for Energy Systems)
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32 pages, 2217 KiB  
Article
Statistical Comparison of Time Series Models for Forecasting Brazilian Monthly Energy Demand Using Economic, Industrial, and Climatic Exogenous Variables
by André Luiz Marques Serrano, Gabriel Arquelau Pimenta Rodrigues, Patricia Helena dos Santos Martins, Gabriela Mayumi Saiki, Geraldo Pereira Rocha Filho, Vinícius Pereira Gonçalves and Robson de Oliveira Albuquerque
Appl. Sci. 2024, 14(13), 5846; https://doi.org/10.3390/app14135846 - 4 Jul 2024
Cited by 6 | Viewed by 1451
Abstract
Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, [...] Read more.
Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033. Full article
(This article belongs to the Special Issue Advanced Forecasting Techniques and Methods for Energy Systems)
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Review

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25 pages, 2212 KiB  
Review
A Review of Smart Photovoltaic Systems Which Are Using Remote-Control, AI, and Cybersecurity Approaches
by Andreea-Mihaela Călin (Comșiț), Daniel Tudor Cotfas and Petru Adrian Cotfas
Appl. Sci. 2024, 14(17), 7838; https://doi.org/10.3390/app14177838 - 4 Sep 2024
Cited by 3 | Viewed by 1862
Abstract
In recent years, interest in renewable energy and photovoltaic systems has increased significantly. The design and implementation of photovoltaic systems are various, and they are in continuous development due to the technologies used. Photovoltaic systems are becoming increasingly complex due to the constantly [...] Read more.
In recent years, interest in renewable energy and photovoltaic systems has increased significantly. The design and implementation of photovoltaic systems are various, and they are in continuous development due to the technologies used. Photovoltaic systems are becoming increasingly complex due to the constantly changing needs of people, who are using more and more intelligent functions such as remote control and monitoring, power/energy prediction, and detection of broken devices. Advanced remote supervision and control applications use artificial intelligence approaches and expose photovoltaic systems to cyber threats. This article presents a detailed examination of the applications of various remote-control, artificial intelligence, and cybersecurity techniques across a diverse range of solar energy sources. The discussion covers the latest technological innovations, research outcomes, and case studies in the photovoltaics field, as well as potential challenges and the possible solutions to these challenges. Full article
(This article belongs to the Special Issue Advanced Forecasting Techniques and Methods for Energy Systems)
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