Energy Informatics最新文献

The data management method for transmission line defects and hidden dangers enables timely identification and resolution of safety risks in transmission lines, thereby reducing the probability of failures. However, existing data on defects and hidden dangers are often affected by redundant interference, resulting in low mining accuracy. To address this issue, this paper proposes a data management approach for transmission line defects based on an improved isolation forest algorithm. The types of transmission line hidden dangers are analyzed, and a data governance framework for such hidden dangers is established. This framework collects basic data of transmission lines through multiple channels, performs denoising and normalization processing, and constructs a sample dataset for transmission lines. The isolation forest algorithm is selected as the method for detecting hidden trouble data in transmission lines. The algorithm is enhanced using binary particle swarm optimization to improve the detection of hidden trouble data. The detected defect data are applied to the early warning of transmission lines, thereby completing the defect data management process. Experimental results demonstrate that the proposed method can quickly and accurately detect defect data in transmission lines, and the detection results can effectively facilitate risk warning for transmission lines.

In response to the increasingly concealed and sophisticated methods of electricity theft, which are difficult to comprehensively cover and detect in a timely manner, a method for identifying high-risk electricity theft behaviors in low-voltage distribution station areas based on density-based clustering of IoT sensing data is investigated. An intelligent IoT power distribution terminal is deployed at the distribution transformer side within the station area to collect IoT sensor data reflecting electricity consumption behavior. The density-based clustering algorithm is employed to achieve comprehensive clustering of the IoT sensing data by determining the initial cluster centers and iteratively searching and updating these centers. The clustering results of the IoT sensing data are used as input to an LM-BP neural network, which classifies the electricity consumption behavior data in the station area into normal and abnormal categories. Based on optimal matching values, a feature matching approach is applied to determine whether abnormal electricity consumption samples correspond to high-risk theft behaviors, thereby enabling the detection of such behaviors in low-voltage distribution station areas. Experimental results demonstrate that the proposed method can accurately identify high-risk electricity theft behaviors, such as meter bypassing, by leveraging the density-based clustering results of IoT sensing data.

The need for predictive maintenance methods has arisen as a key element in improving operational efficiency, reliability, and life expectancy of photovoltaic (PV) systems and the future complex renewable energy infrastructure sets. The Machine learning (ML) technique is sub part of Artificial Intelligence (AI) technology which has widened their adoption in energy analytics, resulting in numerous studies proposing different algorithms for monitoring, prediction, and prevention of system failures. The overview of these approaches is yet to be exhaustive in the existing literature regarding a metric-based evaluation. In addressing this gap, the article undertakes a structured review of the state-of-the-art recent peer-reviewed literature on predictive maintenance in solar PV systems. Each work will, therefore, be appraised against standardized performance metrics models, which include aspects such as accuracy, precision, recall, F1-score, area under the curve (AUC), and model-specific indicators- Root Mean Square Error (RMSE), latency, and execution delays. A numerical analysis table summarizes and compares the predictive capabilities of techniques such as Random Forest, CatBoost, Convolutional Neural Network (CNN) ensembles, Long Short-Term Memory (LSTM) autoencoders, Supervisory Control and Data Acquisition (SCADA) IoT frameworks, and Digital Twins. High-performing models, such as CatBoost and custom CNN architectures, indicate the effectiveness of hybrid deep learning strategies in fault diagnostics. The review establishes a new benchmark for evaluating PdM systems, readying the bar between academic innovation and real-world deployment. It outlines future research directions including model generalization, real-time edge AI deployment, and integration with climate-aware forecasting systems. This work complements an important entry point for other works by researchers and industry stakeholders’ intent on deploying scalable and resilient predictive maintenance solutions in renewable energy networks.

The increasing volatility of electricity prices, driven by the growing share of renewable energy, calls for new approaches. This paper proposes a dynamic Bayesian network (DBN) method for electricity price interval forecasting. The model uses predicted values of wind power generation, total power generation, and total electricity consumption, along with historical electricity prices, as inputs. The network structure is determined using a greedy search algorithm, and the model parameters are estimated through maximum likelihood estimation (MLE). By treating the predictions of wind power, total generation, and total consumption as reasoning evidence, the method employs joint tree inference to generate discrete states and posterior probabilities for electricity prices, thereby enabling interval forecasting. The DBN-based interval predictions achieve a prediction interval coverage probability (PICP) of 95.24%, a normalized average width (PINAW) of 9.25%, and an accumulated width deviation (AWD) of 0.56%. The effectiveness of the proposed method was evaluated by comparing its predictions with actual electricity prices and with results from both particle swarm optimization-kernel extreme learning machine (PSO-KELM) and long short-term memory (LSTM)-based methods. This innovative approach not only provides prediction intervals but also associates them with corresponding probabilities, offering significant potential to enhance market participants’ decision-making and mitigate price risks.

As a supporting device for electric vehicles, DC charging piles are widely distributed and in large quantities, involving a huge emerging electricity trading market. Ensuring metering accuracy of charging pile is critical to maintaining fair electricity trading. The traditional on-site verification method for charging pile involves high personnel input and low verification efficiency, making it difficult to meet the massive metering verification demand. In this paper, based on knowledge-assisted modal decomposition, the metering error prediction method for charging pile is proposed to remotely locate the charging pile whose metering error is about to exceed the threshold in advance. First, the trend and multi-period characteristics of metering error data—driven by factors such as temperature, humidity, electrical stresses, and user behavior—are analyzed. With an adaptive data imputation method, high-ratio continuous missing values in metering data time series are completed. Then, the error data time series is decomposed into trend, multi-level periodic, and residual terms with the improved seasonal-trend decomposition method. Finally, the trend and multiple periodic terms are predicted based on the support vector regression model, and they are combined to form the error prediction. The effectiveness and superiority of the proposed method are validated through practical application.

In electricity markets, evaluating collusion and market power is a critical challenge for network operators, as such behaviors can disrupt fair competition, induce price volatility, and reduce market efficiency. Effective methods are therefore required to identify and quantify the influence of each market participant on the profits of others. This study aims to assess collusion and market power in large-scale power systems through structural analysis, addressing gaps left by previous research. The proposed methodology relies on two lemmas to model market behavior. Lemma 1 quantifies the effects of various factors on local price changes and generation capacities, while Lemma 2 evaluates their impact on the profit variations of generation units. Using the matrix derived from Lemma 2, which captures profit responses to marginal unit price changes, collusion and market power across the network are assessed. Additionally, three new indicators are introduced to measure market power and collusion in large networks. The approach is applied to a 300-bus system, and detailed analysis demonstrates that changes in generation pricing strategies can substantially influence market power and collusive behavior, providing regulators with a tool for proactive market monitoring and intervention.

The proliferation of energy demand with population growth and associated costs necessitated the development of advanced demand response (DR) strategies in smart grid (SG) environments. This study proposes a novel IoT-enabled Energy Management Controller (IEMC) for smart buildings that addresses the critical challenge of optimal appliance scheduling. The proposed system integrates renewable energy sources (photovoltaic systems), energy storage systems (ESS), and advanced metering infrastructure (AMI) to enable autonomous energy management under Time-of-Use (ToU) pricing schemes. The study categorizes household appliances into schedulable and non-schedulable classes, implementing a hybrid metaheuristic optimization algorithm (HGPO) that combines Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Wind Driven Optimization (WDO) techniques. The multi-objective optimization framework simultaneously addresses four critical performance metrics: electricity cost minimization, peak-to-average ratio (PAR) reduction, carbon emission mitigation, and user comfort (UC) maximization. Extensive simulations demonstrate the superior performance of the proposed IEMC system. The hybrid HGPO algorithm achieves a 57.8% improvement in fitness cost (19.34) compared to traditional GA approaches (39.66), while maintaining the lowest emissions (3.41 tonnes/h) and optimal PAR (10). The system successfully shifts schedulable appliances from peak to off-peak hours, resulting in a 79% reduction in grid import dependency and enhanced battery state-of-charge management with peak utilization reaching 8%. Furthermore, comparative analysis with five other metaheuristic algorithms (GA, Binary PSO, WDO, Ant Colony Optimization, and Bacterial Foraging Algorithm) validates the superiority of the hybrid approach across all performance metrics.

Sustainable Development Goals (SDGS) of the United Nations must be linked with technological advancement and sustainable economic practices. The study provides empirical evidence on how Industry 4.0 (I4.0) technologies and Circular Economy (CE) practices will facilitate faster achievement of the SDGs in G20 economies from 2000 to 2024. The research supports the synergistic effect of I4.0 and CE on sustainability by using panel data analysis by industry and region. The results indicate that integrating I4.0 technologies, including internet usage by individuals, importing ICT goods, exporting high technology products, and manufacturing advanced products, alongside CE principles, leads to high resource utilisation, reduced environmental impacts, and increased innovation. The empirical data demonstrate that Industry 4.0 technology-driven transformations and circular economy practices can enhance sustainability performance, as measured by Adjusted Net Savings, in G20 economies. Digital variables such as internet utilisation, ICT imports, and resource-related sustainable decisions like renewable energy adoption are positively associated with sustainability outcomes- highlighting the synergy between digitalisation and environmental sustainability towards SDGS 7, 9, 12, and 13. To ensure effective integration of technology and CE, policy recommendations include developing more robust digital infrastructure, offering rebates with sustainability-related incentives, and establishing standards for assessment. These efforts by G20 countries should be coordinated with other initiatives, such as the G20 Osaka Blue Ocean Vision and G20 Sustainable Finance Roadmap. Such strategic arrangements can foster green, inclusive development and accelerate the realisation of the SDGS.

To adapt to the complex and volatile environment of the electricity spot market, this study proposes a flexible resource characterization and identification method for Integrated Energy Systems (IES). To address the non-stationarity of multi-energy loads, a Variational Mode Decomposition (VMD) enhanced Temporal Convolutional Network-Graph Convolutional Network-Long Short-Term Memory (TCN-GCN-LSTM) spatiotemporal fusion model is developed, achieving significant improvements in forecasting accuracy compared to benchmark models. For electricity price forecasting, a hybrid Random Forest-Improved Attribute Generalization Importance Value-Complete Ensemble Empirical Mode Decomposition with Sample Entropy-Long Short-Term Memory (RF-IAGIV-CEEMD-SE-LSTM) model is constructed, which combines feature selection, subsequence decomposition, and noise reduction to capture temporal dynamics. Experimental results demonstrate that the proposed models reduce RMSE by up to 42.7% across load types and keep market-clearing deviations within 3% under multiple scenarios. The contributions of this study lie in three aspects: (1) developing a collaborative framework for multi-energy load and price forecasting; (2) proposing advanced spatiotemporal feature extraction and hybrid data preprocessing strategies; and (3) providing case-based validation with diverse market architectures. These results highlight the method’s strong potential for supporting intelligent scheduling and decision-making in modern electricity spot markets.