Energy Informatics最新文献

This study presents a critical and structured review of protection challenges and emerging solutions in renewable-integrated microgrids. The proliferation of distributed energy resources (DERs) has introduced complex protection issues, including bidirectional power flow, low fault currents, mode-dependent dynamics, and communication dependencies. This review systematically examines fault detection, classification, and coordination strategies for both grid-connected and islanded operating conditions from 2020 to 2025. A qualitative comparative analysis is conducted across diverse protection approaches, including conventional relay-based methods, artificial intelligence (AI)-based schemes, fuzzy logic systems, time-frequency analysis, phasor measurement unit (PMU)-assisted techniques, and communication-assisted multi-agent frameworks. The analysis identifies dominant trends toward hybrid, adaptive, and data-driven protection strategies while highlighting persistent gaps in scalability, experimental validation, cybersecurity, and real-world deployment. By synthesizing current research and identifying unresolved challenges, this review provides a clear roadmap for future work toward robust, standardized, and practically deployable microgrid protection systems.

Purpose

Accurate and explainable household load forecasting is critical for demand-side management, tariff-aware scheduling, and reliable smart grid operation. This study introduces a leakage-controlled multi-horizon forecasting pipeline that integrates predictive accuracy with statistical validation, interpretability, robustness, and operational relevance.

Methods

We model multivariate household demand using an hourly smart-meter dataset spanning 14 months (Nov 2022-Jan 2024; N=10,234 time steps), incorporating aligned local weather covariates. A hybrid Transformer-BiLSTM is trained using a multi-output configuration to predict 24-hour and 168-hour trajectories. Hyperparameters are optimized via Bayesian optimization (Optuna) employing chronological train/validation/test splits and a rolling-origin evaluation protocol. Performance is assessed using MAE, RMSE, and MAPE, while pairwise forecast differences are validated using the Diebold–Mariano procedure. Model explanations are generated through SHAP and attention analyses, further complemented by robustness testing (noise and feature dropout) and inference-efficiency measurements.

Results

At the 24-hour horizon, the hybrid model achieves MAE and RMSE values of 0.0539/0.0701 (MAPE 29.7%), yielding performance comparable to N-BEATS (MAE/RMSE 0.0531/0.0684). For the 168-hour horizon, the model attains superior performance among evaluated baselines (MAE/RMSE/MAPE 0.0566/0.0746/30.1%) and demonstrates statistically significant improvements over the Transformer, BiLSTM, and TCN models (Diebold-Mariano, (p<0.001)). SHAP analysis identifies electrical indicators (e.g., voltage, power factor) and meteorological variables (e.g., pressure, temperature statistics) as the dominant drivers of medium-term predictions. Median inference latency remains in the tens-of-milliseconds range per sample, facilitating near real-time application.

Conclusion

Beyond improving forecast accuracy, the proposed framework provides statistically supported and interpretable attributions, remains stable under input degradation, and demonstrates operational value in a tariff-based battery scheduling case study, reducing energy cost by approximately 2.29% over an aggregated multi-week evaluation.

Graphical abstract

Accurate cooling load forecasting is critical for optimizing heating, ventilation, and air conditioning (HVAC) system operations, reducing energy consumption, and advancing building sustainability objectives. This paper introduces the Hybrid Global and Sub-domain Approach (HGSA), a novel forecasting framework that synergistically combines advanced feature engineering, domain-specific data segmentation, and ensemble learning to deliver robust predictions across multiple time horizons. The methodology addresses the inherent complexity of cooling load dynamics by partitioning historical data into complementary domains—global, hourly, day of week, monthly, and temperature-based, each capturing distinct temporal and climatic patterns. HGSA incorporates four feature categories: temporal, weather-related, historical, and periodic factors, with the latter computed at both global and sub-domain levels to enhance pattern recognition. Multiple regression models are trained within each domain, and top-performing models are fused through weighted ensemble optimization to maximize predictive accuracy. Validated on 18 months of real-world data from a commercial building in Hong Kong, HGSA demonstrates substantial improvements over state-of-the-art methods including LSTM, LightGBM, Prophet, Autoregressive models, and Informer across four forecasting scenarios: 1-hour (CV-RMSE: 0.09), 24-hour (CV-RMSE: 0.161), 7-day (CV-RMSE: 0.157), and 1-month (CV-RMSE: 0.188) ahead predictions. The framework’s model-agnostic design ensures flexibility and practical deployability, while its consistently superior performance across short-, medium-, and long-term horizons establishes HGSA as a comprehensive solution for building energy management, HVAC optimization, and strategic energy planning applications.

Cyber-Physical Power Systems (CPPS) increasingly inherit cybersecurity vulnerabilities from industrial control components, yet practitioners lack a CPPS-focused dataset and a consistent way to prioritize remediation beyond generic severity scores. This paper presents a cohesive methodology for collecting, enriching, and modeling CPPS-related CVEs to predict their risk and prioritize remediation. We aggregate over 4,030 ICS-relevant CVEs from public sources (2020–2025) and enrich each with CVSS severity, exploitation data (CISA Known Exploited Vulnerabilities, Exploit Prediction Scoring System), and OT/ICS contextual attributes. Based on the dataset, we develop the two-stage learning framework that achieves the following two goals: (i) the provision of a risk score specific to the CPPS and the indication of the priority of the vulnerabilities, and (ii) an estimated likelihood of exploitation, combining structured indicators with features derived from CVE text. These rankings make triage possible by identifying a set of high priority vulnerabilities while reducing the priority of many others, allowing identification of CPPS components with high-risk issues not accounted for by KEV. The analysis of proposed method is shown to yield more informative prioritization than the severity-only baselines by distinguishing between operationally urgent and non-urgent vulnerabilities. The produced risk levels are intended to be interpretable and deployable, serving as a practical decision support tool for CPPS vulnerability management with the understanding that the true label is uncertain.

The integration of Renewable Energy Sources (RESs) into the electricity grid is essential for obtaining sustainable energy transition, improving energy security, and decreasing carbon footprint. RESs play a crucial role in modernizing the electricity network and facilitating the global movement toward a low-carbon electricity network. Peer-to-peer (P2P) local energy trading appears as an innovative technology that can improve RES integration by enabling prosumers to trade residual energy within the regional energy market. This decentralized trading model not only optimizes the utilization of distributed energy resources but also promotes energy federalization, reduces energy loss, and facilitates renewables integration at the community level. Furthermore, P2P energy trading facilitates the transition to a resilient and adaptive energy environment, enables better management of intermittent renewable generation, and improves grid flexibility. In this regard, this paper proposes a two-stage double-layer decentralized P2P local electricity market for the smart microgrid. The first stage consists of the Nash bargaining game model for optimal unit commitment (BGMU). The goal of this stage is to determine the optimal energy schedules of different customers. The second stage is equipped with the double-layer P2P energy trading market. In the first layer, customers trade energy packages with each other. During this stage, some of the locally produced energies are not successfully matched. In this regard, a second market layer is introduced to handle these residual energy packages. The results show that BGMU can optimally schedule producers’ and consumers’ energy. The BGMU results are transmitted to customers, who will trade these energy packages. Using the proposed P2P energy market’s first layer, the total operating cost of the studied network is reduced by 57% compared to trading with the wholesale energy market. Also, adding the second layer to the P2P energy market leads to a further decrease in the operating cost by 9% compared to the first stage of the presented P2P energy market.

Electricity demand exhibits complex short-term behavioural and temporal dynamics that are increasingly important for operational planning in modern power systems, particularly in developing regions undergoing rapid renewable-energy expansion. This study introduces a data-driven framework that applies technical indicators, adapted from high-frequency financial time-series analysis, to extract trend, momentum and volatility features from high-resolution national electricity demand. Using one year of 15-minute data from Sri Lanka, the framework integrates engineered indicators with gradient-boosting models to identify latent demand structures that are not visible in raw load curves. The results show that momentum- and acceleration-based indicators offer the strongest operational value, with ablation tests revealing accuracy deteriorations exceeding 40% when these features are removed. The model achieved an R² of 0.846 and an overall MAPE of 6.1%, accurately capturing morning ramps, mid-day stabilisation and evening peaks. Forecast deviations during culturally driven events highlight the need for behaviour-sensitive features in developing grids. The extracted demand patterns also reveal operational windows with high potential for storage charging (mid-day) and strategic discharging (evening peaks), demonstrating applications for battery energy-storage optimisation and renewable-integration planning. By showing that finance-inspired indicators enhance both interpretability and predictive performance, this study provides a replicable methodology for grid operators seeking low-cost, data-driven tools for short-term decision support. The framework offers actionable insights for generation scheduling, reserve planning, demand-response design and the efficient utilisation of storage assets in emerging, renewables-constrained power systems.

This paper presents the design and validation of a modular interface architecture for automated testing of IoT devices. The proposed system integrates asynchronous communication, real-time environmental data visualization, and support for multiple network protocols such as MQTT and HTTP/REST. The backend is implemented in Python using FastAPI, while the frontend utilizes a custom GUI developed in Tkinter. Testing was performed under normal, boundary, and failure conditions, with both simulated and physical sensor inputs. Results show reduced manual effort, improved reproducibility, and compatibility with CI/CD workflows.

Electricity markets increasingly rely on residential demand-side flexibility to integrate renewables and stabilize the grid. While dynamic tariffs can unlock short-term flexibility, they expose households to a risk–reward trade-off. This paper quantifies how home battery storage reshapes the trade-off across residential energy services modeled with three different load types (elastic, interruptible and non-interruptible). Using load profiles from a German utility and an optimal-control scheduling framework under mixed dynamic tariffs, we evaluate cost and risk impacts over a range of storage sizes. Three results stand out. First, small batteries deliver most of the value: a capacity of about 20% of average daily demand captures roughly two-thirds of attainable savings while already lowering bill risk. Second, cost reduction potential is heterogeneous across devices: Elastic loads profit the most from additional storage capacities; Non-interruptible and Interruptible loads profit less. Third, overall returns diminish and effectively plateau near a capacity of 60% of average daily demand. These findings offer actionable guidance: pair dynamic tariffs with modest storage to achieve substantial savings and risk reduction—especially in low-flexibility or strongly market-aligned households—and avoid over-investment in regards to diminishing returns.

The Government of Uttarakhand has undertaken significant initiatives to promote renewable energy development to meet growing demand while safeguarding the fragile Himalayan ecosystem. In this context, the present study evaluates the feasibility of deploying large-scale solar power parks in Uttarakhand using a hybrid Analytical Hierarchy Process (AHP)–TOPSIS multi-criteria decision-making framework. Five potential locations—Dehradun, Nainital, Haridwar, Almora, and Udham Singh Nagar—are assessed based on a comprehensive STEEP (Social, Technological, Economic, Environmental, and Political) framework comprising 18 sub-criteria. AHP is employed to determine the relative importance of the criteria, while TOPSIS is used to rank the candidate sites according to their closeness to the ideal solution. The results indicate Udham Singh Nagar as the most feasible location for solar park deployment, owing to its high solar irradiation potential, developed infrastructure, land availability, and comparatively lower environmental sensitivity. Haridwar and Dehradun follow as the second and third preferred locations, respectively. The results demonstrate the effectiveness of the hybrid AHP–TOPSIS approach in integrating technical, environmental, and socio-economic considerations for solar site selection. The study provides practical insights for policymakers and planners to support informed decision-making for sustainable solar energy investments, contributing to energy security, ecological conservation, and regional sustainable development.

Accurate prediction of the remaining useful life (RUL) of power transformers is critical for ensuring the safety, reliability, and intelligent operation of modern power systems. However, transformer operating data are typically nonlinear, nonstationary, and multi-source coupled, posing significant challenges for conventional models in feature extraction and temporal modeling. To overcome these limitations, this study proposes a hybrid predictive framework that integrates signal decomposition, intelligent optimization, and deep learning—the SSA-CEEMDAN-Transformer-BiGRU model. First, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to perform multi-scale decomposition of the transformer oil temperature series, effectively isolating noise, periodic fluctuations, and long-term degradation trends. Second, the Sparrow Search Algorithm (SSA) is utilized to conduct global adaptive optimization of key hyperparameters in the Transformer-BiGRU network, thereby improving convergence speed and generalization capability. Finally, the Transformer module captures global temporal dependencies through its multi-head self-attention mechanism, while the BiGRU network characterizes local dynamic variations via a bidirectional gated structure. Experimental results on the ETTh2 dataset demonstrate that the proposed model substantially outperforms traditional statistical and deep learning approaches in both prediction accuracy and stability, achieving an R² of 0.9721 and an MSE of 0.031 on the test set. Ablation and feature-importance analyses further confirm the critical contribution of the CEEMDAN and SSA modules to overall performance enhancement. The findings indicate that the proposed methodology provides a high-precision, interpretable, and practically deployable solution for intelligent condition monitoring and lifetime management of power transformers.