Energy Informatics最新文献

{"title":"Double-layer decentralized framework for local energy trading in the smart energy distribution network","authors":"Amirhamzeh Farajollahi,&nbsp;Meysam Jalalvand,&nbsp;Ali Nemati Mofarrah","doi":"10.1186/s42162-026-00623-y","DOIUrl":"10.1186/s42162-026-00623-y","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00623-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven method for predicting short-term electricity demand using technical indicators","authors":"W. D. Gammanpila,&nbsp;A. C. Gammanpila,&nbsp;A. H. T. S. Kularathna,&nbsp;N. K. Jayasooriya","doi":"10.1186/s42162-026-00645-6","DOIUrl":"10.1186/s42162-026-00645-6","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00645-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and implementation of an interface architecture for automated IoT device testing platforms","authors":"Ainur Mukhiyadin,&nbsp;Anara Akmoldina,&nbsp;Karlygash Tainova,&nbsp;Darkhan Abdrahmanov","doi":"10.1186/s42162-026-00629-6","DOIUrl":"10.1186/s42162-026-00629-6","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00629-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147336500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk and reward: evaluating household energy storage for optimizing demand-side flexibility under dynamic tariffs","authors":"Justus Ameling,&nbsp;Robin Thomas Derzbach,&nbsp;Gunther Gust,&nbsp;Christoph Michael Flath","doi":"10.1186/s42162-025-00602-9","DOIUrl":"10.1186/s42162-025-00602-9","url":null,"abstract":"<div><p>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: <i>Elastic loads profit the most from additional storage capacities</i>; 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.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00602-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of deploying solar power park using hybrid AHP-TOPSIS analysis: case study of Uttarakhand","authors":"Pankaj Aswal,&nbsp;Atul Rawat,&nbsp;Nitin Sundriyal,&nbsp;Tejpal Jhajharia","doi":"10.1186/s42162-026-00632-x","DOIUrl":"10.1186/s42162-026-00632-x","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00632-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A power transformer residual life prediction model based on SSA-CEEMDAN-Transformer-BiGRU","authors":"Wenhao Liu,&nbsp;Jijian Ma,&nbsp;Qiaojun Chen,&nbsp;Hu Qu","doi":"10.1186/s42162-026-00634-9","DOIUrl":"10.1186/s42162-026-00634-9","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00634-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equipment protection and anomaly warning method of intelligent substation based on homologous recording and multi-source data","authors":"Haibo Zhang,&nbsp;Hongying Xing,&nbsp;Shicheng Duan","doi":"10.1186/s42162-026-00631-y","DOIUrl":"10.1186/s42162-026-00631-y","url":null,"abstract":"<div>\u0000 \u0000 <p>In smart substations, equipment protection and abnormal warning are crucial to the safe and stable operation of the power grid. In existing research, traditional methods such as LSTM (Long Short-Term Memory Network) are time-consuming and require large computing resources, and SVM (Support Vector Machine) is easy to fall into local optimization and limited generalization ability when processing high-dimensional data, making it difficult to efficiently realize deep feature mining and accurate early warning of multi-source data. In this study, it is proposed to achieve accurate early warning through “synchronization data of similar equipment” (i.e., simultaneous collection of operating data of the same type of equipment, such as load current and oil temperature of multiple transformers on the same bus, so as to facilitate mutual verification in case of abnormality) and multi-source data fusion technology. Firstly, high-precision sensors are used to collect electrical and non-electric data such as voltage, current, equipment temperature, and vibration in real time, and various data features are integrated into a unified vector (such as combining power trend and vibration frequency characteristics) through feature-level fusion, and then redundancy is removed by dimensionality reduction algorithms such as PCA. The core model uses the “Whale Optimization Extreme Learning Machine” (WOA-ELM): WOA simulates the initial parameters of the Whale Predation Behavior Optimization Extreme Learning Machine (ELM), and the ELM exerts fast learning and strong generalization capabilities to deeply mine the processed multi-source data features. Experiments show that compared with the traditional model, the accuracy of equipment anomaly identification is improved by about 20%, and the early warning response time is shortened by 30%, which significantly improves the efficiency and reliability of equipment protection in intelligent substation. It not only provides a stronger guarantee for the safe operation of equipment, but also shows potential application value in the early abnormal warning system, which can help the power system achieve more efficient preventive maintenance.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00631-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"nZEB beyond prediction in smart built environments: formalising engineering knowledge through modular explainable machine learning","authors":"Nuno Soares Domingues","doi":"10.1186/s42162-025-00613-6","DOIUrl":"10.1186/s42162-025-00613-6","url":null,"abstract":"<div><p>This paper demonstrates how explainable machine learning (XAI) can be operationalised as a methodological pathway for formalising engineering knowledge from high-frequency building operational data. We propose a modular pipeline that combines feature engineering, ensemble and sequence learners, SHAP attribution and uncertainty quantification to convert raw sensor streams into machine-readable knowledge artefacts (JSON schema) suitable for automation workflows such as fault detection and demand response. Using a monitored nearly Zero-Energy Building (nZEB) in Lisbon (12 months, 5-minute resolution), we (i) report model performance (LightGBM, Random Forest, SVR, Linear Regression, and LSTM) under time-aware 70/15/15 split and 5-fold temporal cross-validation; (ii) present SHAP-based global and local attribution analyses that identify stable seasonal drivers; and (iii) provide computational cost (training and inference times) and uncertainty quantification. Results show ensemble models achieve superior short-term forecasting accuracy while producing consistent, actionable attributions that can be encoded as reusable artefacts. We close by describing a JSON artefact schema and outlining how these artefacts could be integrated within digital twins and supervisory control systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-025-00613-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of maximum power point tracking in a wind-solar hybrid power plant by neural networks for reducing total harmonic distortion (THD)","authors":"Hasan mohammadi,&nbsp;Mehdi Radmehr,&nbsp;Tohid Nouri,&nbsp;Alireza Ghafouri","doi":"10.1186/s42162-026-00620-1","DOIUrl":"10.1186/s42162-026-00620-1","url":null,"abstract":"<div>\u0000 \u0000 <p>The economic viability of distributed energy resources (DERs) like wind turbines and photovoltaic (PV) units is hampered by their low conversion efficiencies and ineffective energy management. This research aims to improve Maximum Power Point Tracking (MPPT) in a hybrid wind-PV power system to reduce voltage and current oscillations by using the properties of DC link behavior. The innovation in this study stems from creating an integrated MPPT supervisor that responds to both the partial shading of PV modules and the variability of wind simultaneously and without utilizing current or voltage sensors. An artificial neural network (ANN) is used for this purpose, and its parameters are tuned by the Particle Swarm Optimization (PSO) algorithm. The proposed strategy was implemented in simulations compared to dynamic P&amp;O, standalone ANN, and hybrid PSO-ANN frameworks. Based on the simulations performed, the PSO-ANN controller outperforms other methods by achieving better efficiency in MPPT when using DC link voltage as input and lowering Total Harmonic Distortion (THD). Also, the controller reduces the DC link voltage ripple while attenuating current and voltage THD to 3% and 2%, respectively. Moreover, during islanded operation, the controller decreases the distortion by 1.27%, showing enhanced system stability without traditional feedback control.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00620-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of the connection node and power of battery energy storage system in distribution electric network","authors":"Tokhir Makhmudov","doi":"10.1186/s42162-026-00637-6","DOIUrl":"10.1186/s42162-026-00637-6","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the global increase in the share of renewable energy sources in the share of generation in distribution electric networks around the world, an acute problem of power fluctuations has arisen. Battery energy storage systems make it possible to smooth out peak loads, compensate for the instability of renewable energy generation, provide backup power and optimize network operation, including in the context of reducing total active power losses in the network. However, their implementation is associated with a number of technical, economic and regulatory challenges that require an integrated approach to integration and management. The purpose of this article is to select a connection node and capacity of a battery storage system in a distribution electric network in terms of minimization of total daily active energy losses in the network. An algorithm is presented that allows selecting a connection node for the storage system in terms of minimization of total daily active energy losses. Using a test circuit of a distribution electric network as an example, a modeling of the connection of a battery energy storage system was carried out, the values of active power losses and the capacities of the battery system were obtained.</p>\u0000 </div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00637-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}