Energies

15 pages, 3077 KiB

Open AccessArticle

Gas Content and Gas Occurrence Mechanism of Deep Coal Seams in the Shenfu-Linxing Block

by Litao Ma, Fan Yang, Jianghao Yang, Yi Cui, Wei Wang, Cheng Liu, Bo Zhang, Jiang Yang and Shu Tao

Energies 2025, 18(3), 699; https://doi.org/10.3390/en18030699 (registering DOI) - 3 Feb 2025

Abstract

The Shenfu-Linxing block in the Ordos Basin holds abundant deep coalbed methane (CBM) resources, which can alleviate gas shortages and aid dual carbon target achievement. Considering isothermal adsorption traits and parameters like vitrinite reflectance, temperature, pressure, and water saturation, a prediction model for [...] Read more.

The Shenfu-Linxing block in the Ordos Basin holds abundant deep coalbed methane (CBM) resources, which can alleviate gas shortages and aid dual carbon target achievement. Considering isothermal adsorption traits and parameters like vitrinite reflectance, temperature, pressure, and water saturation, a prediction model for adsorbed and free gas content was formulated. This model helps to reveal the deep CBM occurrence mechanism in the Shenfu-Linxing block. Results show that deep CBM exists in both adsorbed and free states, with adsorbed gas initially increasing then decreasing, and free gas rising then stabilizing as burial depth increases. A critical transition depth for total CBM content exists, shallowing with higher water saturation. As depth increases, temperature and pressure evolution results in a “rapid growth—slow growth—stability—slow decrease” pattern in total gas content. Adsorbed gas resides in micropores, while free gas occupies larger pores. Full article

(This article belongs to the Special Issue Exploration and Development of Unconventional Oil and Gas Resources: Latest Advances and Prospects: 2nd Edition)

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30 pages, 3108 KiB

Open AccessArticle

Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks

by Wenjie Pan, Jun Han, Chao Cai, Haofei Chen, Hong Liu and Zhengyang Xu

Energies 2025, 18(3), 698; https://doi.org/10.3390/en18030698 (registering DOI) - 3 Feb 2025

Abstract

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing [...] Read more.

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing uncertainties in renewable energy generation and load demand. By leveraging a dynamic interaction between sensitivity scores and temporal system conditions, the framework achieves efficient and resilient operation under extreme variability scenarios. Key methodological innovations include the incorporation of a social force model-based sensitivity mapping technique, a layered optimization approach balancing system-wide and localized decisions, and a robust uncertainty set to safeguard performance against distributional shifts. The framework is validated using a synthesized test system, incorporating realistic renewable generation profiles, load patterns, and energy storage dynamics. Results demonstrate a significant improvement in hosting capacity, with system-wide enhancements of up to 35% and a 50% reduction in renewable curtailment. Moreover, sensitivity-driven resource deployment ensures efficient utilization of flexibility resources, achieving a peak allocation efficiency of 90% during critical periods. This research provides a comprehensive tool for addressing the challenges of renewable integration and grid stability in modern power systems, offering actionable insights for resource allocation strategies under uncertainty. The proposed methodology not only advances the state-of-the-art in sensitivity-based optimization but also paves the way for scalable, resilient energy management solutions in high-renewable penetration scenarios. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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21 pages, 761 KiB

Open AccessArticle

The Impact of Trade Openness on Carbon Emissions: Empirical Evidence from Emerging Countries

by Rui Zhou, Shu Guan and Bing He

Energies 2025, 18(3), 697; https://doi.org/10.3390/en18030697 (registering DOI) - 3 Feb 2025

Abstract

Emerging countries are the main source of new CO₂ emissions and the major net carbon importers, and they have also become an important part of the global trade pattern. In this study, the impact of trade openness on CO₂ emissions was [...] Read more.

Emerging countries are the main source of new CO₂ emissions and the major net carbon importers, and they have also become an important part of the global trade pattern. In this study, the impact of trade openness on CO₂ emissions was investigated by approaches such as fully modified least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group-autoregressive distributive lag (PMG-ARDL) methods. Further estimations were conducted by employing methods such as DCCEMG (dynamic common-correlated effect mean group) and Driscoll–Kray to strengthen the robustness of the results. Moreover, the Granger causality between trade openness and CO₂ emissions was tested by using the Dumitrescu–Hurlin method. Conclusions can be drawn as follows: First, economic growth, energy consumption, trade openness, and CO₂ emissions are all interconnected in the long term. Specifically, higher levels of economic growth and trade openness are associated with lower CO₂ emissions, whereas energy consumption contributes to higher emissions. However, in the short term, economic growth and energy consumption lead to an increase in CO₂ emissions, while trade openness does not have a significant impact. Moreover, there is a two-way Granger causality between trade openness and CO₂ emissions. Additionally, economic growth and energy consumption have an indirect effect on CO₂ emissions by influencing trade openness. Given these findings, emerging market countries should focus on enhancing their service sectors, promoting technological advancements, and fostering international collaboration in green technologies. By actively engaging in efforts to combat climate change, these countries reach a point where trade expansion and carbon reduction are achieved. Full article

(This article belongs to the Special Issue Energy Transition and Environmental Sustainability: 3rd Edition)

25 pages, 2336 KiB

Open AccessArticle

Drying Time, Energy and Exergy Efficiency Prediction of Corn (Zea mays L.) at a Convective-Infrared-Rotary Dryer: Approach by an Artificial Neural Network

by Yousef Abbaspour-Gilandeh, Safoura Zadhossein, Mohammad Kaveh, Mariusz Szymanek, Sahar Hassannejad and Krystyna Wojciechowska

Energies 2025, 18(3), 696; https://doi.org/10.3390/en18030696 (registering DOI) - 3 Feb 2025

Abstract

Energy consumption in the drying industry has made drying an energy-intensive operation. In this study, the drying time, quality properties (color, shrinkage, water activity and rehydration ratio), specific energy consumption (S.E.C), thermal, energy and exergy efficiency of corn drying using a hybrid dryer [...] Read more.

Energy consumption in the drying industry has made drying an energy-intensive operation. In this study, the drying time, quality properties (color, shrinkage, water activity and rehydration ratio), specific energy consumption (S.E.C), thermal, energy and exergy efficiency of corn drying using a hybrid dryer convective-infrared-rotary (CV-IR-D) were analyzed. In addition, the energy parameters and exergy efficiency of corn were predicted using the artificial neural network (ANN) technique. The experiments were conducted at three rotary rotation speeds of 4, 8 and 12 rpm, drying temperatures of 45, 55 and 65 °C, and infrared power of 0.25, 0.5 and 0.75 kW. By increasing drying temperature, infrared power and rotary rotation speed, the drying time, S.E.C and water activity decreased while the D_eff, energy, thermal and exergy efficiency increased. In addition, the highest values of rehydration ratio and redness (a*) and the lowest values of shrinkage, brightness (L*), yellowness (b*) and color changes (ΔE) were obtained at an infrared power of 0.5 kW, air temperature of 55 °C and rotation speed of 8 rpm. The range of changes in S.E.C, energy, thermal and exergy efficiency during the corn drying process was 5.05–28.15 MJ/kg, 3.26–29.29%, 5.5–32.33% and 21.22–55.35%. The prediction results using ANNs showed that the R for the drying time, S.E.C, thermal, energy and exergy data were 0.9938, 0.9906, 0.9965, 0.9874 and 0.9893, respectively, indicating a successful prediction. Full article

(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)

15 pages, 3147 KiB

Open AccessArticle

Transmission Line Icing Prediction Based on Physically Guided Fast-Slow Transformer

by Feng Wang and Ziming Ma

Energies 2025, 18(3), 695; https://doi.org/10.3390/en18030695 (registering DOI) - 3 Feb 2025

Abstract

To improve the accuracy of the icing prediction model for overhead transmission lines, a physics-guided Fast-Slow Transformer icing prediction model for overhead transmission lines is proposed, which is based on the icing prediction model with meteorological input characteristics. First, the ice cover data [...] Read more.

To improve the accuracy of the icing prediction model for overhead transmission lines, a physics-guided Fast-Slow Transformer icing prediction model for overhead transmission lines is proposed, which is based on the icing prediction model with meteorological input characteristics. First, the ice cover data is segmented into different time resolutions through Fourier transform; a transformer model based on Fourier transform is constructed to capture the local and global correlations of the ice cover data; then, according to the calculation model of the comprehensive load on the conductor and the conductor state equation, the variation law of ice thickness, temperature, wind speed, and tension is analyzed, and the model loss function is constructed according to the variation law to guide the training process of the model. Finally, the sample mixing enhancement algorithm is used to reduce the overfitting problem and improve the generalization performance of the prediction model. The results show that the proposed prediction model can consider the mechanical constraints in the ice growth process and accurately capture the dependence between ice cover and meteorology. Compared with traditional prediction models such as LSTM (Long Short-Term Memory) networks, its mean square error, mean absolute error, and mean absolute percentage error are reduced by 0.464–0.674, 0.41–0.53, and 8.87–11.5%, respectively, while the coefficient of determination (R²) is increased by 0.2–0.29. Full article

(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)

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15 pages, 2188 KiB

Open AccessArticle

Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement

by Jie Fang, Wenwu Li and Dunchu Chen

Energies 2025, 18(3), 694; https://doi.org/10.3390/en18030694 (registering DOI) - 3 Feb 2025

Abstract

With the increasing penetration of distributed generation (DG), the supply–demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening [...] Read more.

With the increasing penetration of distributed generation (DG), the supply–demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. It replaces the traditional energy storage model with this model and then solves the EV and SOP collaborative planning model using a second-order conical planning algorithm with the objective function of minimizing the annual integrated cost. At the same time, the flexibility of the distribution network is analyzed by two indicators: power supply and demand balance and branch load margin. Finally, the optimization method proposed in this paper is analyzed and validated on an improved IEEE 33-node distribution system. Example results show that the planning method proposed in this paper can effectively reduce the annual comprehensive operating cost of distribution networks, meet the flexibility index, and be conducive to improving the economy and flexibility of distribution network operation. Full article

(This article belongs to the Section E: Electric Vehicles)

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16 pages, 1350 KiB

Open AccessArticle

An Improved Methodology to Locate Faults in Onshore Wind Farm Collector Systems

by Moisés Davi, Alailton Júnior, Caio Grilo, Talita Cunha, Leonardo Lessa, Mário Oleskovicz and Denis Coury

Energies 2025, 18(3), 693; https://doi.org/10.3390/en18030693 (registering DOI) - 3 Feb 2025

Abstract

This paper explores the growing integration of Inverter-Based Resources (IBRs) into power systems and their effects on fault diagnosis strategies. Notably, the technical literature lacks assessments of the impacts and proposals for solutions for phasor-based fault location tasks, considering faults occurring within wind [...] Read more.

This paper explores the growing integration of Inverter-Based Resources (IBRs) into power systems and their effects on fault diagnosis strategies. Notably, the technical literature lacks assessments of the impacts and proposals for solutions for phasor-based fault location tasks, considering faults occurring within wind power plants, i.e., in their collector systems. In this context, this study evaluates the performance of six state-of-the-art phasor-based fault location methods, which are tested through simulations in a realistic wind farm modeled using PSCAD software. These simulations cover a wide range of fault scenarios, including variations in fault types, resistances, inception angles, locations, and wind farm generation levels. The proposed methodology, which combines the various fault location methods tailored to specific fault types, results in a substantial improvement, achieving an average fault location error of 1.89%, reflecting a 92% reduction in error compared to conventional methods. Additionally, the approach consistently maintains low fault location errors across collector busbars, regardless of circuit topology, highlighting its robustness, adaptability, and potential for widespread implementation in fault diagnosis systems within wind farms. Full article

(This article belongs to the Topic Advances in the Monitoring and Diagnosis of Faults in Wind Power Plants)

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27 pages, 8624 KiB

Open AccessArticle

A Decision-Making Tool for Sustainable Energy Planning and Retrofitting in Danish Communities and Districts

by Muhyiddine Jradi

Energies 2025, 18(3), 692; https://doi.org/10.3390/en18030692 (registering DOI) - 2 Feb 2025

Abstract

This study presents a novel framework for city-level energy planning and retrofitting, tailored to Danish cities and neighborhoods. The framework addresses the challenges of large-scale urban energy modeling by integrating automated processes for data collection, energy demand prediction, and renewable energy integration. It [...] Read more.

This study presents a novel framework for city-level energy planning and retrofitting, tailored to Danish cities and neighborhoods. The framework addresses the challenges of large-scale urban energy modeling by integrating automated processes for data collection, energy demand prediction, and renewable energy integration. It combines open-source simulation tools and validated datasets, enabling efficient and scalable predictions of energy performance across urban areas, including streets, districts, and entire cities, with minimal user input. The key components include data collection and demand modeling, energy resource estimation, performance gap evaluation, and the design of retrofitting strategies with renewable energy integration. The DanCTPlan energy planning tool, developed based on this framework, was applied to two case studies in Denmark: a single street with 101 buildings and a district comprising five streets with 1284 buildings. In the single-street case, retrofitting all buildings to meet current regulations resulted in a 60.8% reduction in heat demand and a 5.8% reduction in electricity demand, with significant decreases in peak energy demands. The district-level retrofitting measures led to a 29.5% reduction in heat demand and a 2.4% reduction in electricity demand. Renewable energy scenarios demonstrated that photovoltaic systems supplying 30% of electricity demand and solar thermal systems meeting 10% of heating demand would require capacities of 2218 kW and 3540 kW, respectively. The framework’s predictive capabilities and flexibility position it as a robust tool to support decision-makers in developing sustainable and cost-effective energy strategies, paving the way toward establishing energy-efficient and positive energy districts. Full article

(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)

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14 pages, 1977 KiB

Open AccessArticle

Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices

by Michał Dominik Stasiak, Żaneta Staszak, Joanna Siwek and Dawid Wojcieszak

Energies 2025, 18(3), 691; https://doi.org/10.3390/en18030691 (registering DOI) - 2 Feb 2025

Abstract

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, [...] Read more.

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, the possibility of an effective prediction of the direction of future changes in the price of crude oil is especially significant. Most existing works focus on the analysis of daily closing prices. This kind of approach results, on the one hand, in losing important information about the dynamics of changes during the day. On the other hand, it does not allow for the modelling of short-term price changes that are especially important in cases of financial derivatives having crude oil as their base instrument. The goal of the following article is the analysis of possible applications of a binary–temporal representation in the modelling and construction of effective decision support systems on the crude oil market. The analysis encompasses all researched state models, e.g., those applying mean and trend analysis. Also, the selection of parameters was optimized for Brent crude oil rates. The presented research confirms the high effectiveness of our state modelling system in predicting oil prices on a level that allows for the construction of financially effective investment decision support systems. The obtained results were verified based on proper backtests from different quotation periods. The presented results can be used both in scientific analyses and in the construction of investment support tools for the crude oil market. Full article

(This article belongs to the Section C: Energy Economics and Policy)

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23 pages, 3699 KiB

Open AccessArticle

Improved Multi-Objective Strategy Diversity Chaotic Particle Swarm Optimization of Ordered Charging Strategy for Electric Vehicles Considering User Behavior

by Shuyi Zhao, Chenshuo Ma and Zhiao Cao

Energies 2025, 18(3), 690; https://doi.org/10.3390/en18030690 (registering DOI) - 2 Feb 2025

Abstract

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user [...] Read more.

With the development of the EV industry, the number of EVs is increasing, and the random charging and discharging causes a great burden on the power grid. Meanwhile, the increasing electricity bills reduce user satisfaction. This article proposes an algorithm that considers user satisfaction to solve the charging and discharging scheduling problem of EVs. This article adds an objective function to quantify user satisfaction and addresses the issues of premature local optima and insufficient diversity in the MOPSO algorithm. Based on the performance of different particles, the algorithm assigns elite particle, general particle, and learning particle roles to the particles and assigns strategies for maintaining search, developing search, and learning search, respectively. In order to avoid falling into local optima, chaotic sequence perturbations are added during each iteration process avoiding premature falling into local optima. Finally, case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results show that the proposed algorithm is capable of achieving up to 23% microgrid load reduction and up to 20% improvement in convergence speed compared to other algorithms. It is superior to other algorithms in solving the problem of orderly charging and discharging of electric vehicles and has strong usability and feasibility. Full article

(This article belongs to the Special Issue Leveraging Flexibility Resources to Enhance Renewable Energy Integration and Grid Stability)

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31 pages, 1288 KiB

Open AccessReview

The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects

by Emmanuel Ejuh Che, Kang Roland Abeng, Chu Donatus Iweh, George J. Tsekouras and Armand Fopah-Lele

Energies 2025, 18(3), 689; https://doi.org/10.3390/en18030689 (registering DOI) - 2 Feb 2025

Abstract

Although the impact of integrating solar and wind sources into the power system has been studied in the past, the chaos caused by wind and solar energy generation has not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts in [...] Read more.

Although the impact of integrating solar and wind sources into the power system has been studied in the past, the chaos caused by wind and solar energy generation has not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts in using prediction models have developed real-time monitoring of variability and machine learning predictive algorithms in contrast to the conventional methods of studying variability. This study focused on the causes and types of variability, challenges, and mitigation strategies used to minimize variability in grids worldwide. A summary of the top ten cases of countries that have successfully managed variability in their electrical power grids has been presented. Review shows that most of the success cases embraced advanced energy storage, grid upgrading, and flexible energy mix as key technological and economic strategies. A seven-point conceptual framework involving all energy stakeholders for managing variability in power system networks and increasing variable renewable energy (VRE)-grid integration has been proposed. Long-duration energy storage, virtual power plants (VPPs), smart grid infrastructure, cross-border interconnection, power-to-X, and grid flexibility are the key takeaways in achieving a reliable, resilient, and stable grid. This review provides a useful summary of up-to-date research information for researchers and industries investing in a renewable energy-intensive grid. Full article

(This article belongs to the Section F1: Electrical Power System)

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20 pages, 5717 KiB

Open AccessArticle

On-Line Insulation Monitoring Method of Substation Power Cable Based on Distributed Current Principal Component Analysis

by Haobo Yang, Jingang Wang, Pengcheng Zhao, Chuanxiang Yu, Hongkang You and Jinyao Dou

Energies 2025, 18(3), 688; https://doi.org/10.3390/en18030688 (registering DOI) - 2 Feb 2025

Abstract

Monitoring the insulation condition of power cables is essential for ensuring the safe and stable operation of the substation power supply system. Leakage current is an important indicator of insulation performance of power cables. However, the application of leakage current monitoring methods in [...] Read more.

Monitoring the insulation condition of power cables is essential for ensuring the safe and stable operation of the substation power supply system. Leakage current is an important indicator of insulation performance of power cables. However, the application of leakage current monitoring methods in substations is limited due to issues such as neutral line shunting on the load side and the spatial isolation of the phase-to-neutral line in the power cabinet. This paper proposes an insulation monitoring method based on distributed current principal component analysis for power cables in substations. Firstly, the leakage current of substation power cable is measured by a distributed current extraction method, and the cable insulation condition is preliminarily judged. Then, considering the problem of measurement error interference in the process of distributed current synthesis, an evaluation method of power cable insulation state based on principal component analysis of distributed current is proposed. To verify the feasibility of the proposed method, both simulation and laboratory tests were conducted. The results indicate that the proposed method can effectively measure the leakage current of power cables in substations and realize the accurate distinction between measurement error and cable insulation degradation characteristics. The method offers a novel idea for insulation monitoring of substation power cables. Full article

(This article belongs to the Special Issue Advances in Design, Operation and Maintenance Technology for Electric Power Equipment)

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17 pages, 6425 KiB

Open AccessArticle

Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism

by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao and Yiwen Li

Energies 2025, 18(3), 687; https://doi.org/10.3390/en18030687 (registering DOI) - 2 Feb 2025

Abstract

The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes [...] Read more.

The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). First, this method delves into the correlations between various regions within the target city, establishing intricate coupling relationships among them. Subsequently, the FastDTW algorithm is employed to construct an adjacency matrix, capturing the spatiotemporal correlation among different urban regions. Finally, the ASTGCN model is applied to predict the power load of each region, which can accurately capture the spatiotemporal characteristics of the power load. The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps. Full article

(This article belongs to the Section E: Electric Vehicles)

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22 pages, 20223 KiB

Open AccessArticle

Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy

by Kangji Li, Shiyi Zhou, Mengtao Zhao and Borui Wei

Energies 2025, 18(3), 686; https://doi.org/10.3390/en18030686 (registering DOI) - 2 Feb 2025

Abstract

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in [...] Read more.

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in buildings leads to insufficient data sampling for model training, which is currently an important factor affecting the performance of short-term electrical load prediction. To address this issue, by using peak data clustering and knowledge transfer from similar buildings, a short-term electrical load forecasting method is proposed. First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. Second, for peak/valley data clusters, an instance-based transfer learning (IBTL) strategy is used to transfer similar data from multi-source domains to enhance the target prediction’s accuracy. During the process, a two-stage similar data selection strategy is applied based on Wasserstein distance and locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, is designed to construct the predictive model. The performance of proposed method is validated on a public dataset. Results show that the data clustering and transfer learning method reduces the error by 49.22% (MAE) compared to the Elman model. Compared to the same transfer learning model without data clustering, the proposed approach also achieves higher prediction accuracy (1.96% vs. 2.63%, MAPE). The proposed method is also applied to forecast hourly/daily power demands of two real campus buildings in the USA and China, respectively. The effects of data clustering and knowledge transfer are both analyzed and compared in detail. Full article

(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)

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22 pages, 719 KiB

Open AccessArticle

ICT-Driven Strategies for Enhancing Energy Efficiency in G20 Economies: Moderating the Role of Governance in Achieving Environmental Sustainability

by Zohaib Zahid, Jijian Zhang, Chongyan Gao and Judit Oláh

Energies 2025, 18(3), 685; https://doi.org/10.3390/en18030685 (registering DOI) - 2 Feb 2025

Abstract

Achieving environmental sustainability has become a global priority, with energy efficiency (EE) emerging as a critical pathway. This study examines the influence of information and communication technology service exports (ICT) on EE by integrating the moderating role of regulatory quality. We employ a [...] Read more.

Achieving environmental sustainability has become a global priority, with energy efficiency (EE) emerging as a critical pathway. This study examines the influence of information and communication technology service exports (ICT) on EE by integrating the moderating role of regulatory quality. We employ a super-slack-based measure (Super-SBM) and generalized least squares models in G20 economies throughout 2001–2023. The findings show that the average EE is 0.855, which indicates a potential for further improvement of 14.50%. The findings further show that ICT is positively related to EE, and regulatory quality delivers a conducive environment for the adoption of technologies to optimize energy usage. The findings also indicate a synergistic effect between ICT and regulatory quality, which can lead to substantial improvements in EE, emphasizing the importance of governance in facilitating technological advancements. The findings highlight the role of renewable energy and economic openness in shaping EE. Furthermore, Argentina and South Africa achieved the highest EE, reflecting their proximity to the efficient frontier. In robust tests, this study verifies its results using the generalized method of moments, panel-corrected standard error, and feasible generalized least squares models. The findings suggest that ICT and governance perspectives can provide valuable insights for policymakers aiming to enhance energy sustainability through digital transformation and institutional reforms. Full article

(This article belongs to the Topic Enabling Strategies and Policies Toward a Sustainable Environment, 2nd Edition)

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21 pages, 346 KiB

Open AccessArticle

Blessing or Curse? The Impact of the Penetration of Industrial Robots on Green Sustainable Transformation in Chinese High-Energy-Consuming Industries

by Yueqi Sun, Junlong Ti, Fang Yang and Hsing Hung Chen

Energies 2025, 18(3), 684; https://doi.org/10.3390/en18030684 (registering DOI) - 1 Feb 2025

Abstract

The rapid development and widespread application of artificial intelligence (AI) and robots have profoundly impacted the economy, society, and the environment. This article focuses on the relationship between industrial robots and green sustainable transformation in high-energy-consuming industries. Through the Poisson distribution fixed effect, [...] Read more.

The rapid development and widespread application of artificial intelligence (AI) and robots have profoundly impacted the economy, society, and the environment. This article focuses on the relationship between industrial robots and green sustainable transformation in high-energy-consuming industries. Through the Poisson distribution fixed effect, the negative binomial fixed-effect model, and the two-way fixed-effect model, we found that the penetration of industrial robots in high-energy-consuming enterprises (HEEs) has a significant and positive effect on green innovation. In particular, we verified that total factor productivity and ESG, included in the zero-inflation model, have a breakthrough-accelerating role in the application of industrial robots to promote green sustainable transformation. Further analysis indicated that the adoption of industrial robots is also positively correlated with the improvement of corporate green sustainable transformation in non-state-owned enterprises, but state-owned enterprises are not sensitive. In the classification of segmented industries, only the metallurgical industry demonstrates the empowering role of green sustainable transformation. This article provides a new avenue for reshaping the low-carbon green sustainable transformation strategy of HEEs, as well as useful insights, supporting the achievement of carbon peak and carbon neutrality by promoting the application of industrial robots and further improving total factor productivity and ESG performance. Full article

(This article belongs to the Special Issue Available Energy and Environmental Economics: Volume II)

18 pages, 3180 KiB

Open AccessArticle

Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator

by Xiaoxuan Wu, De Tian, Huiwen Meng and Yi Su

Energies 2025, 18(3), 683; https://doi.org/10.3390/en18030683 (registering DOI) - 1 Feb 2025

Abstract

Parameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish identification speed, subpar accuracy, and [...] Read more.

Parameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish identification speed, subpar accuracy, and susceptibility to local optimization. In light of these challenges, this paper proposes a distributed parameter identification framework based on intelligent algorithms. The proposed approach involves the deployment of SSA, DBO, and PSO algorithms, leveraging golden sine ratio and Gaussian variation strategies for multi-parameter optimization and performance enhancement. Second, the optimal solutions of each intelligent algorithm are aggregated to achieve overall optimization performance enhancement. The efficacy of the proposed method is substantiated by a 6 MW PMSWG parameter identification practice simulation result, which demonstrates its superiority. The proposed method was shown to identify parameters more quickly and effectively than the underlying algorithms, which is of great significance for condition monitoring, fault diagnosis, and robust control of the PMSWG. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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15 pages, 1569 KiB

Open AccessArticle

Comparative Analysis of Smart Building Solutions in Europe: Technological Advancements and Market Strategies

by Negar Mohtashami, Nils Sauer, Rita Streblow and Dirk Müller

Energies 2025, 18(3), 682; https://doi.org/10.3390/en18030682 (registering DOI) - 1 Feb 2025

Abstract

This paper provides a comprehensive comparative analysis of smart building solution providers within Europe, emphasizing the technological advancements and market strategies employed by companies selected for the study. As energy efficiency becomes a critical focus due to rising global energy demands and climate [...] Read more.

This paper provides a comprehensive comparative analysis of smart building solution providers within Europe, emphasizing the technological advancements and market strategies employed by companies selected for the study. As energy efficiency becomes a critical focus due to rising global energy demands and climate change concerns, smart building technologies have emerged as pivotal in optimizing energy use and enhancing occupant comfort. This study examines 19 products from 15 prominent manufacturers, categorized into six product categories: smart thermostats, smart valves, HVAC control, data acquisition and energy management software, smart home ecosystems, and home energy management systems. Using a comparative assessment matrix and SWOT analysis, the paper evaluates these products across five key areas: service impacts, market penetration, investment topics, business models, and value propositions. Findings highlight a strong focus of manufacturers in energy efficiency and comfort services, while identifying opportunities for improvement in energy flexibility and health integration. This analysis aims to guide stakeholders in strategic planning and decision-making, offering insights into the current and future landscape of the smart building solutions market. Full article

(This article belongs to the Special Issue Emerging Trends in Energy and Environmental Design Integrating New Services and Tools for Smart Cities and Smart Buildings)

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20 pages, 22279 KiB

Open AccessReview

Enhancing Offshore Wind Turbine Integrity Management: A Bibliometric Analysis of Structural Health Monitoring, Digital Twins, and Risk-Based Inspection

by Thomas Bull, Min Liu, Linda Nielsen and Michael Havbro Faber

Energies 2025, 18(3), 681; https://doi.org/10.3390/en18030681 (registering DOI) - 1 Feb 2025

Abstract

The grand challenge of sustainable development, increased demands for resilient critical infrastructure systems, and cost efficiency calls for thinking and acting “out of the box”. We must strive to search for, identify, and utilize new and emerging technologies and new combinations of existing [...] Read more.

The grand challenge of sustainable development, increased demands for resilient critical infrastructure systems, and cost efficiency calls for thinking and acting “out of the box”. We must strive to search for, identify, and utilize new and emerging technologies and new combinations of existing technologies that have the potential to improve present best practices. In integrity management of, e.g., bridge, offshore, and marine structures, relatively new technologies have shown substantial potentials for improvements that not least concern structural health monitoring (SHM), digital twin (DT)-based structural and mechanical modeling, and risk-based inspection (RBI) and maintenance planning (RBI). The motivation for the present paper is to investigate and document to what extent such technologies in isolation or jointly might have the potential to improve best practices for integrity management of offshore wind turbine structures. In this pursuit, the present paper conducts a comprehensive bibliometric analysis to explore the current landscape of advanced technologies within the offshore wind turbine industry suitable for integrity management. It examines the integration of these technologies into future best practices, taking into account normative factors like risk, resilience, and sustainability. Through this analysis, the study sheds light on current research trends and the degree to which normative considerations influence the application of RBI, SHM, and DT, either individually or in combination. This paper outlines the methodology used in the bibliometric study, including database selection and search term criteria. The results are presented through graphical representations and summarized key findings, offering valuable insights to inform and enhance industry practices. These key findings are condensed into a road map for future research and development, aimed at improving current best practices by defining a series of projects to be undertaken. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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