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Renewable Energy System Technologies: 3rd Edition
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Renewable Energy System Technologies: 3rd Edition

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: 10 April 2026 | Viewed by 5612

Special Issue Editor

Prof. Dr. Tek-Tjing Lie

E-Mail Website
Guest Editor
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: AI applications to power systems; power system control and operation; smart grids; renewable energy resources; energy management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to share the success of our Special Issues “Renewable Energy System Technologies” and “Renewable Energy System Technologies: 2nd Edition”.

In the first volume, we successfully published 9 papers:
https://www.mdpi.com/journal/energies/special_issues/T7V6ENEPGZ

In the second volume, we successfully published 10 papers:
https://www.mdpi.com/journal/energies/special_issues/8LIW67O0LZ

We are now preparing to launch the third volume of this Special Issue, “Renewable Energy System Technologies: 3rd Edition”.

Renewable energy resources, such as solar photovoltaic (PV) and wind turbine generation, are completely dependent on nature (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, their outputs are stochastic in nature, and new technologies need to be developed and applied to overcome intermittency issues as well as big data in real-time.

Integrated system modelling methods and concepts are needed to study the self-organization, complexity, emergent properties, and dynamical behaviour of complex systems for their holistic understanding, management, and development based primarily on neural networks, fuzzy and soft systems/fuzzy cognitive maps, network modelling, and mathematics. Other advanced applications in the computational early detection of mastitis and computer-based decision support systems for complex systems are also needed. Due to the scale of the network and the amount of data that needs to be digitized, new technologies such as techniques in data mining and AI approaches are needed to analyze and predict the behaviour of these complex systems.

Prof. Dr. Tek-Tjing Lie
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • big data
  • solar PV
  • wind turbine generation
  • intermittent
  • real time

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Related Special Issues

Published Papers (5 papers)

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41 pages, 4123 KB  
Article
Short-Term Wind Power Forecasting with Transformer-Based Models Enhanced by Time2Vec and Efficient Attention
by Djayr Alves Bispo Junior, Gustavo de Novaes Pires Leite, Enrique Lopez Droguett, Othon Vinicius Cavalcanti de Souza, Lucas Albuquerque Lisboa, George Darmiton da Cunha Cavalcanti, Alvaro Antonio Villa Ochoa, Alexandre Carlos Araújo da Costa, Olga de Castro Vilela, Leonardo José de Petribú Brennand, Guilherme Ferretti Rissi, Giovanni Moura de Holanda and Tsang Ing Ren
Energies 2025, 18(23), 6162; https://doi.org/10.3390/en18236162 - 24 Nov 2025
Viewed by 430
Abstract
Accurate wind power forecasting is essential to optimize wind farm operations and ensure the stable integration of renewable energy into the grid. This study explores Transformer-based architectures to address the challenges of wind variability and temporal dependencies in short-term forecasting. A sensitivity analysis [...] Read more.
Accurate wind power forecasting is essential to optimize wind farm operations and ensure the stable integration of renewable energy into the grid. This study explores Transformer-based architectures to address the challenges of wind variability and temporal dependencies in short-term forecasting. A sensitivity analysis on model architecture is conducted, incorporating Time2Vec—a temporal encoding technique that captures complex temporal patterns. In addition, we replace the standard FullAttention mechanism with ProbSparse Attention, FlowAttention and FlashAttention, resulting in the Informer, Flowformer and Flashformer models, to improve computational efficiency while maintaining predictive accuracy. The novelty of this work lies in applying FlashAttention within the context of wind power forecasting and integrating Time2Vec into the Informer, Flowformer and Flashformer models. We propose four architectures—T2V-Transformer, T2V-Informer, T2V-Flowformer, and T2V-Flashformer—and compare them against benchmark models: Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and DLinear. Real-world data from a wind farm in the Northeast of Brazil is used under two forecasting scenarios. In Scenario A, T2V-Transformer, T2V-Informer and T2V-Flashformer achieved Improvement over Reference RMSE (IoR-RMSE) scores of 17.73%, 17.59% and 16.67%, respectively. In Scenario B, T2V-Flowformer and T2V-Flashformer reached 27.84% and 27.45%, respectively. These results confirm the effectiveness of the proposed models in advancing short-term wind power forecasting. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)
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15 pages, 132684 KB  
Article
Overcoming Variable Illumination in Photovoltaic Snow Monitoring: A Real-Time Robust Drone-Based Deep Learning Approach
by Amna Mazen, Ashraf Saleem, Kamyab Yazdipaz and Ana Dyreson
Energies 2025, 18(19), 5092; https://doi.org/10.3390/en18195092 - 25 Sep 2025
Cited by 1 | Viewed by 531
Abstract
Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using [...] Read more.
Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image processing algorithm for snow segmentation. We benchmarked several segmentation models, including MASK R-CNN and the state-of-the-art SAM2 segmentation model. YOLOv11n-seg was selected for its optimal balance of speed and accuracy, achieving 0.99 precision and 0.80 recall. To overcome the unreliability of static thresholding under changing lighting, various dynamic methods were evaluated. Otsu’s algorithm proved most effective, reducing the absolute error of the mean in SCP estimation to just 1.1%, a significant improvement over the 13.78% error from the previous fixed-thresholding approach. The integrated system was successfully validated for real-time performance on live drone video streams, demonstrating a highly accurate and scalable solution for autonomous snow monitoring on PV systems. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)
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26 pages, 2794 KB  
Article
Benchmarking Transformer Variants for Hour-Ahead PV Forecasting: PatchTST with Adaptive Conformal Inference
by Vishnu Suresh
Energies 2025, 18(18), 5000; https://doi.org/10.3390/en18185000 - 19 Sep 2025
Cited by 2 | Viewed by 1736
Abstract
Accurate hour-ahead photovoltaic (PV) forecasts are essential for grid balancing, intraday trading, and renewable integration. While Transformer architectures have recently reshaped time series forecasting, their application to short-term PV prediction with calibrated uncertainty remains largely unexplored. This study provides a systematic benchmark of [...] Read more.
Accurate hour-ahead photovoltaic (PV) forecasts are essential for grid balancing, intraday trading, and renewable integration. While Transformer architectures have recently reshaped time series forecasting, their application to short-term PV prediction with calibrated uncertainty remains largely unexplored. This study provides a systematic benchmark of five Transformer variants (Autoformer, Informer, FEDformer, DLinear, and PatchTST) evaluated on a five-year, rooftop PV dataset (5 kW peak) against an unseen 12-month test set. All models are trained within a pipeline using a 48-h rolling input window with cyclical temporal encodings to ensure comparability. Beyond point forecasts, we introduce Adaptive Conformal Inference (ACI), a distribution-free and adaptive framework, to quantify uncertainty in real time. The results demonstrate that PatchTST, through its patch-based temporal tokenization, delivers superior accuracy (MAE = 0.194 kW, RMSE = 0.381 kW), outperforming both classical persistence and other Transformer baselines. When coupled with ACI, PatchTST achieves 86.2% empirical coverage with narrow intervals (0.62 kW mean width) and probabilistic scores (CRPS = 0.54; Winkler = 1.86) that strike a balance between sharpness and reliability. The findings establish that combining patch-based Transformers with adaptive conformal calibration provides a novel and viable route to risk-aware PV forecasting. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)
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23 pages, 1250 KB  
Article
Strategies to Incentivize the Participation of Variable Renewable Energy Generators in Balancing Markets
by Hugo Algarvio and Vivian Sousa
Energies 2025, 18(11), 2800; https://doi.org/10.3390/en18112800 - 27 May 2025
Viewed by 1071
Abstract
Balancing markets (BMs) play a crucial role in ensuring the real-time equilibrium between electricity demand and supply. The current requirements for participation in BMs often overlook the characteristics and capabilities of variable renewables, limiting their effective integration. The increasing penetration of variable renewables [...] Read more.
Balancing markets (BMs) play a crucial role in ensuring the real-time equilibrium between electricity demand and supply. The current requirements for participation in BMs often overlook the characteristics and capabilities of variable renewables, limiting their effective integration. The increasing penetration of variable renewables necessitates adjustments in the design of BMs to support the transition toward carbon-neutral power systems. This study examines the levels of active market participation for a wind power producer (WPP) in the Iberian Electricity Market and the Portuguese BMs. In addition to exploring current market dynamics, the study tests one methodology proposed by the Danish Transmission System Operator to support the participation of variable renewables in BMs, the P90, and two new methods based on the full cost balancing concept. These methodologies incentivize WPPs to minimize imbalances by allowing market participation only if imbalances remain within a 10% deadband of annual hours (P90), hourly offers (D90), or both (DP90). The results indicate that participating in the secondary capacity market, particularly for downward capacity, is the most profitable strategy. This participation enhances the value of wind power by over 42%. However, in most methodologies, the WPP failed to deliver nearly 100% of its allocated capacity approximately 1% of the time. In contrast, the D90 approach limited the maximum deviation to 10%, demonstrating the highest reliability among the evaluated methods. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)
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33 pages, 4008 KB  
Systematic Review
Applications of the Digital Twin and the Related Technologies Within the Power Generation Sector: A Systematic Literature Review
by Saeid Shahmoradi, Mahmood Hosseini Imani, Andrea Mazza and Enrico Pons
Energies 2025, 18(21), 5627; https://doi.org/10.3390/en18215627 - 26 Oct 2025
Cited by 1 | Viewed by 1450
Abstract
Digital Twin (DT) technology has emerged as a valuable tool for researchers and engineers, enabling them to optimize performance and enhance system efficiency. This paper presents a comprehensive Systematic Literature Review (SLR) following the PRISMA framework to explore current applications of DT technology [...] Read more.
Digital Twin (DT) technology has emerged as a valuable tool for researchers and engineers, enabling them to optimize performance and enhance system efficiency. This paper presents a comprehensive Systematic Literature Review (SLR) following the PRISMA framework to explore current applications of DT technology in the power generation sector while highlighting key advancements. A new framework is developed to categorize DTs in terms of time-scale horizons and applications, focusing on power plant types (emissive vs. non-emissive), operational behaviors (including condition monitoring, predictive maintenance, fault detection, power generation prediction, and optimization), and specific components (e.g., power transformers). The time-scale is subdivided into a six-level structure to precisely indicate the speed and time range at which it is used. More importantly, each category in the application is further subcategorized into a three-level framework: component-level (i.e., fundamental physical properties and operational characteristics), system-level (i.e., interaction of subsystems and optimization), and service-level (i.e., value-adding service outputs). This classification can be utilized by various parties, such as stakeholders, engineers, scientists, and policymakers, to gain both a general and detailed understanding of potential research and operational gaps. Addressing these gaps could improve asset longevity and reduce energy consumption and emissions. Full article
(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)
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