Energy Informatics最新文献

System restoration is aimed at ensuring continuity of the electric supply to all loads in a distribution system under abnormal conditions without violating electrical-constraints. This adds the feature of “self-healing” to the distribution system to make it as smart system. This paper presents a literature survey of published research techniques on electric supply restoration over the period 1981–2024. Four categories of distribution systems with different attributes are proposed by the present authors to compare fairly among these techniques through implementation and running the necessary codes for each restoration technique. Comparisons are concerned with contribution, adopted technique, test model, advantages and disadvantages as well as utilization of renewables. To meet the electrical-constraints on electric supply restoration, fifteen challenges are selected, reviewed and discussed within the comparisons. The algorithms based on graph theory showed better performance regarding the challenges related to minimizing the energy-not-supplied, achieving self-healing dream, preventing feeder overloading and maintaining the voltage profile within limits when compared with other algorithms. The algorithms based on linear and nonlinear programming showed better performance concerning the challenges related to minimizing restoration time and preventing in-supply load shedding when compared with other algorithms. The algorithms based on heuristics and metaheuristics showed better performance concerning the challenges related to system configuration, generating optimal sequence of switches, minimizing the number of ordered switches and reducing the restoration cost when compared with other algorithms. The future trends of the supply restoration in smart distribution systems are also discussed. The present survey is concluded with a summary of the findings from the literature survey and outlines potential directions for future research. It highlights the key opportunities to support researchers in advancing more intelligent restoration strategies for electric supply in smart distribution systems.

Accurate and robust wind power prediction for wind farms could significantly decrease the substantial effect on grid operating safety caused by integrating high-permeability intermittent power supplies into the power grid. The article introduces a new wind power multistep prediction model combining Variational Mode De-composition (VMD) with the Long Short-Term Enhanced Forget Gate (LSTM_EFG) network. The VMD is occupied to break down the initial wind power and speed data into various sub-layers. The LSTM_EFG network predicts the low-frequency sub-layers extracted from the VMD. In contrast, the Artificial Bee Colony optimization algorithm fine-tunes the network for the high-frequency sub-layers acquired from the VMD-LSTM-EFG model. The high performance of projected methods in multistep prediction was evaluated by comparing them with eight different models. Results from four experiments show that: (a) the projected model exhibits the most superior multistep prediction performance out of all models tested; (b) in comparison to other models, the proposed model proves to be more efficient and resilient in capturing trend information. The implementation of accurate wind power prediction models continues to pose challenges due to the unpredictable, sudden, and seasonal changes in wind patterns.

In the contemporary landscape of complex industrial processes, the efficient utilization of renewable energy has emerged as a crucial concern, captivating the attention of researchers, industries, and policymakers alike. However, integrating these renewable energy sources into traditional AC distribution networks has proven to be a formidable challenge. Against this backdrop, this paper presents an innovative optimal control method tailored for energy routers (ERs) in flexible AC/DC distribution networks. To effectively harness the capabilities of ERs, a Long-Short-Term Memory (LSTM) network augmented with an attention mechanism is employed. The attention mechanism allows the LSTM network to focus on the most relevant information in the time-series data, thereby improving the prediction accuracy. Subsequently, an optimization model is constructed to maximize the utilization of renewable energy by ERs. To validate the effectiveness of the proposed method, a two-week field test was conducted as part of an energy retrofit project in China. When compared with conventional methods, the proposed approach has been shown to enhance the local absorption of PV generation by over 24.7%.

Accurate electricity load forecasting is essential for the stability, efficiency, and sustainability of modern power systems. However, individual forecasting models often lack generalization across temporal and regional variations and offer limited interpretability. This study proposes a comprehensive meta-learning-based forecast combination framework to enhance both prediction accuracy and model transparency. Using hourly load data from 20 European countries spanning 2018 to 2024, the framework incorporates time-aware features such as hour of the day, day of the week, month, and public holidays. Ten diverse base models—including XGBoost, LightGBM, Random Forest, and LSTM—are trained globally, from which the top five performers are selected (based on R², MAE, and MAPE) and fed into five meta-learners: Ridge Regression, Lasso, Random Forest, Gradient Boosting, and MLP. These meta-models are trained using both model predictions and engineered time features. Experimental results demonstrate superior performance, with the best-performing meta-learner (Random Forest Regressor) achieving a coefficient of determination (R²) of 0.9998 and a Mean Absolute Percentage Error (MAPE) of 0.79%, significantly outperforming traditional ensemble methods. Furthermore, the inclusion of lag features and 5-fold cross-validation led to substantial improvements across all models, including dramatic reductions in MAE (up to 87%), MAPE (up to 88%), and MSE (up to 97%), along with near-perfect R² scores (~ 1.000). Additionally, SHAP-based explainability reveals the contribution of individual time-based features and the influence of each base model within the ensemble, thereby enhancing transparency and supporting practical decision-making.

The proliferation of electric vehicles (EVs) requires accurate and context-aware forecasting of charging times to maximize user satisfaction and optimize energy resource planning. Existing predictive models, however, sometimes ignore dynamic elements including battery health degradation, ambient temperature variations, and charger variability by depending just on static statistics and simple heuristics. This work presents a robust artificial intelligence-based system integrating data-driven modelling with computer vision for automatic recognition of EV models and adaptive charging time estimate. Multi-angle visual data helps to optimize a refined ResNet50 architecture for strong EV classification. The model guarantees consistent performance under real-world conditions including occlusion, lighting variation, and non-standard viewing angles by using transfer learning, residual feature propagation, and extensive data augmentation. With a top-1 classification accuracy of 96%, an F1-score of 96%, and a recall of 95%, experimental data show that the proposed ResNet50 model beats conventional models including VGG16, VGG19, and YOLOv8. Following recognition, a module driven by metadata retrieves important battery properties. These are then fed into a dynamic power-flow-based charging time calculator that modulates predictions depending on real-time criteria including state-of- charge (SoC), charger rating, and ambient conditions. Through reduction of idle charging times and improvement of user-level decision-making, this combined approach offers a scalable and intelligent answer to EV infrastructure planning. The integration of deep learning-based image recognition with real-time parameterized analytics demonstrates strong potential for advancing smart transportation systems and enabling more adaptive, personalized electric mobility experiences.

Energy communities consist of decentralized energy production, storage, consumption, and distribution and are gaining traction in modern power systems. However, these communities may increase the vulnerability of the grid to cyber threats. We propose an anomaly-based intrusion detection system to enhance the security of energy communities. The system leverages LSTM autoencoders to detect deviations from normal operational patterns in order to identify anomalies induced by attacks or faults. Operational data for training and evaluation are derived from a Simulink-based model of an energy community. The results show that the autoencoder-based intrusion detection system achieves good detection performance across multiple attack scenarios, up to 0.9270 and 0.9735 in precision and recall respectively. We also demonstrate potential for real-world application of the system by training a federated model that enables distributed intrusion detection while preserving data privacy.

Under the background of global energy transformation and the integration of digital economy, energy enterprises’ digital investment faces the challenges of uncertain return cycle and lack of data asset pricing mechanism. By constructing a system dynamics model, this paper reveals the dynamic mechanism of data-driven digital investment decision-making of energy enterprises. The research shows that: the value of data assets forms a self reinforcing cycle through the return reinvestment loop, and its scale expansion is regulated by the dynamic balance between the cost constraint and the value inhibition loop; The improvement of market risk perception, the robustness of the trading market, the increase of energy policy intensity and the weakening of peer competition can significantly improve the cumulative profits of enterprises; Adaptive investment strategy has more advantages than fixed investment strategy, but the timing of strategy transformation needs to be accurately controlled. The simulation results provide a basis for enterprises to optimize the data investment path. It is suggested to build a data-driven dynamic investment system, deepen the operation of data assets, and call on the policy side to improve the data factor market system and incentive measures, so as to jointly promote the strategic transformation of energy enterprises to data centers.

This paper proposes an integrated knowledge graph-based power dispatching model for emergency response, combining Markov chain-based text preprocessing, entity-extracted knowledge graph construction, and case-based reasoning optimization - a novel approach that enhances both real-time decision-making and system security. First, a Markov chain-based method effectively removes redundant information from power anomaly event texts, improving entity extraction accuracy. Subsequently, a knowledge graph is constructed to precisely identify key entities, enabling the creation of a structured power emergency plan database. Finally, case-based reasoning matches real-time anomalies with historical cases, facilitating the rapid generation of optimal dispatching schemes. The experiments demonstrate that the proposed model achieves high efficiency (with an average dispatching time < 50 s) and reliability (exhibiting a failure blowout rate below 0.1%), thereby significantly improving power grid safety. The proposed framework advances intelligent power system dispatching by integrating text analytics, knowledge representation, and adaptive reasoning.

Achieving Sustainable Development Goal 7 (SDG7: Affordable and Clean Energy) and Sustainable Development Goal 13 (SDG13: Climate Action) requires advancing renewable energy systems with enhanced sustainability and resilience. Traditional Photovoltaic (PV) planning often focuses on average energy output, overlooking critical metrics such as consistency, variability, and long-term performance. This study analyzes three consecutive years (2017–2019) to assess the impact of climate variability on the energy trends of three PV technologies, fixed PV, Concentrated PV (CPV), and Dual Axis Tracking PV (DATPV), across six global cities. Sustainability scores were calculated using a GIS-based metric that captures energy consistency, intermonthly variability, and climatic adaptability, providing a technical evaluation of long-term system stability under varying weather conditions. The results reveal Cairo and Riyadh as top performers, achieving sustainability scores of 0.87 and 0.70, respectively, for fixed PV in 2019. In Madrid, DATPV systems excelled with sustainability scores reaching 0.39 in 2019, leveraging abundant solar resources. Meanwhile, Beijing’s fixed PV systems demonstrated exceptional stability, maintaining scores of 0.58 across all years, reflecting the region’s consistent solar conditions. By integrating sustainability metrics, this study offers a comprehensive framework for evaluating PV systems under changing climatic conditions, advancing SDG7 by ensuring reliable energy access and SDG13 by promoting resilient, climate-adaptive renewable energy solutions.

Amidst growing environmental imperatives, digital technologies have emerged as pivotal enablers of sustainable transformation in the logistics sector, particularly by improving energy efficiency and reducing greenhouse gas emissions. Despite increasing recognition of their importance, the concrete mechanisms and pathways through which digitalization drives energy conservation and emission reduction at the enterprise level remain insufficiently understood. Addressing this substantive gap, this study aims to systematically elucidate how digital technologies empower logistics enterprises to achieve low-carbon transformation. Using SF Holding—a leading digitalized logistics firm in China—as a representative case, we develop and empirically validate an integrated framework encompassing green innovation, energy substitution, and operational efficiency. Employing Grey Relational Analysis, we quantitatively investigate how six key factors—R&D investment, cumulative granted patents, newly granted patents, new energy vehicle adoption, photovoltaic power generation, and enterprise digitalization degree—impact two core environmental performance indicators: greenhouse gas emission intensity and energy consumption intensity. The results demonstrate that cumulative technological capability and the degree of enterprise digitalization are especially influential in promoting emission reduction and energy efficiency. By clarifying the micro-level mechanisms—such as technological accumulation, clean energy integration, and operational optimization—this study advances theoretical understanding of digitalization-driven green transformation in logistics and offers actionable insights for both policymakers and industry practitioners seeking to foster low-carbon logistics through digital innovation.