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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 (10 April 2026) | Viewed by 18701

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Special Issue Editor

Prof. Dr. Tek-Tjing Lie

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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

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Keywords

big data
solar PV
wind turbine generation
intermittent
real time

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Related Special Issues

Renewable Energy System Technologies in Energies (9 articles)
Renewable Energy System Technologies: 2nd Edition in Energies (14 articles)

Published Papers (9 papers)

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Research

Jump to: Review, Other

30 pages, 3417 KB

Open AccessArticle

Integrated Thermoelectric Power Generation and Membrane-Based Water Desalination Using Low-Grade Thermal Energy

by Oranit Traisak, Pranjal Kumar, Ratan Kumar Das, Sara Vahaji, Yihe Zhang, Varun Velankar and Abhijit Date

Energies 2026, 19(4), 1054; https://doi.org/10.3390/en19041054 - 18 Feb 2026

Cited by 3 | Viewed by 573

Abstract

This study experimentally investigates a novel hybrid system integrating thermoelectric generators (TEGs) with direct contact membrane distillation (DCMD) for simultaneous low-grade heat recovery, electricity generation, and water desalination. Commercial TEG modules were sandwiched between heat spreaders to transfer thermal energy from a source (approx. 140 °C) to a cooling sink, driving saline water evaporation through a hydrophobic membrane. A validated mathematical model showed strong agreement with the experimental results. The system achieved freshwater mass fluxes of 8–9.5 kg/m²/h and electrical power outputs density of 25–35 W/m². Increasing heat input (450–700 W) significantly enhanced freshwater production and electrical output, improving the Gain Output Ratio (GOR) and reducing Specific Energy Consumption (SEC). While higher feed salinity (up to 35,000 ppm) measurably declined mass flux and thermal efficiency, thermoelectric generation and thermal resistance remained largely unaffected. Energy and exergy efficiencies showed moderate sensitivity to operating conditions, while the Water–Electrical Energy Cogeneration Index (WEeCI) increased at high salinity, highlighting the robust contribution of electricity generation. These results demonstrate the potential of the TEG–DCMD system for the sustainable co-generation of water and power from industrial waste heat or renewable thermal sources. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)

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18 pages, 12622 KB

Open AccessArticle

Flexible Solar Panel Recognition Using Deep Learning

by Mingyang Sun and Dinh Hoa Nguyen

Energies 2026, 19(4), 872; https://doi.org/10.3390/en19040872 - 7 Feb 2026

Viewed by 756

Abstract

Solar panels are an important device converting light energy into electricity not only from the sun but also from artificial light sources such as light emitting diodes (LEDs) or lasers. Recent advances in solar cell technologies enable them to be flexible, allowing them to be attached to things with different sizes and shapes. Therefore, it is challenging for AI-equipped systems to automatically recognize and distinguish flexible solar panels from other surrounding objects in realistic, complicated environments. Traditional recognition methods usually suffer from low recognition accuracy and high computational cost. Hence, this paper proposes a deep learning method for solar panel recognition using a complete work flow that includes data acquisition and dataset construction, YOLOv8-based model training, real-time solar panel recognition, and extended functionality. The proposed method demonstrates the accurate identification of realistic flat and flexible solar panels, including bent and partially shaded panels, with a mean average precision (mAP)@0.5 of 99.4% and an mAP@0.5:0.95 of 90.4%. The Pareto front for the multi-objective loss function minimization problem is also investigated to determine the optimal set of weighting parameters for the loss components. Furthermore, another functionality is added to detect the sizes of different solar panels if multiple ones co-exist. These features provide a promising foundation for further usage of the proposed deep learning approach to recognize flexible solar panels in realistic contexts. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)

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27 pages, 5197 KB

Open AccessArticle

Dynamic TRM Estimation with Load–Wind Uncertainty Using Rolling Window Statistical Analysis for Improved ATC

by Uchenna Emmanuel Edeh, Tek Tjing Lie and Md Apel Mahmud

Energies 2026, 19(3), 844; https://doi.org/10.3390/en19030844 - 5 Feb 2026

Cited by 1 | Viewed by 978

Abstract

The rapid integration of renewable energy sources (RES), particularly wind, together with fluctuating demand, has introduced significant uncertainty into power system operation, challenging traditional approaches for estimating Transmission Reliability Margin (TRM) and Available Transfer Capability (ATC). This paper proposes a fully adaptive TRM estimation framework that leverages rolling-window statistical analysis of net-load forecast errors to capture real-time uncertainty fluctuations. By continuously updating both the confidence factor and window length based on evolving forecast-error statistics, the method adapts to changing grid conditions. The framework is validated on the IEEE 30-bus system with 80 MW wind (42.3% penetration) and assessed for scalability on the IEEE 118-bus system (40.1% wind penetration). Comparative analysis against static TRM, fixed-confidence rolling-window, and Monte Carlo Simulation (MCS)-based methods shows that the proposed approach achieves 88.0% reliability coverage (vs. 81.8% for static TRM) while providing enhanced transfer capability for 31.5% of the operational day (7.5 h). Relative to MCS, it yields a 20.1% lower mean TRM and a 2.5% higher mean ATC, with an adaptation ratio of 18.8:1. Scalability assessment confirms preserved adaptation (12.4:1) with sub-linear computational scaling (1.82 ms to 3.61 ms for a 3.93× network size increase), enabling 1 min updates interval. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)

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41 pages, 4123 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1517

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 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

Open AccessEditor’s ChoiceArticle

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 992

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 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

Open AccessFeature PaperArticle

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 12 | Viewed by 3974

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 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

Open AccessFeature PaperArticle

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 1476

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 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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Review

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29 pages, 3682 KB

Open AccessReview

Data Centers as a Driving Force for the Renewable Energy Sector

by Parsa Ziaei, Oleksandr Husev and Jacek Rabkowski

Energies 2026, 19(1), 236; https://doi.org/10.3390/en19010236 - 31 Dec 2025

Cited by 1 | Viewed by 4366

Abstract

Modern data centers are becoming increasingly energy-intensive as AI workloads, hyperscale architectures, and high-power processors push power demand to unprecedented levels. This work examines the sources of rising energy consumption, including evolving IT load dynamics, variability, and the limitations of legacy AC-based power-delivery architectures. These challenges amplify the environmental impact of data centers and highlight their growing influence on global electricity systems. The paper analyzes why conventional grid-tied designs are insufficient for meeting future efficiency, flexibility, and sustainability requirements and surveys emerging solutions centered on DC microgrids, high-voltage DC distribution, and advanced wide-bandgap power electronics. The review further discusses the technical enablers that allow data centers to integrate renewable energy and energy storage more effectively, including simplified conversion chains, coordinated control hierarchies, and demand-aware workload management. Through documented strategies such as on-site renewable deployment, off-site procurement, hybrid energy systems, and flexible demand shaping, the study shows how data centers are increasingly positioned not only as major energy consumers but also as key catalysts for accelerating renewable-energy adoption. Overall, the findings illustrate how the evolving power architectures of large-scale data centers can drive innovation and growth across the renewable energy sector. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies: 3rd Edition)

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Other

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33 pages, 4008 KB

Open AccessEditor’s ChoiceSystematic 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 7 | Viewed by 3264

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 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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