Energy Informatics最新文献

{"title":"Hierarchical multilevel smart energy monitoring system","authors":"Levent Kılıç","doi":"10.1186/s42162-026-00697-8","DOIUrl":"10.1186/s42162-026-00697-8","url":null,"abstract":"<div><p>Energy management in industrial facilities requires the integration of operational data, utility systems, and enterprise-level decision-making processes. Although ISA-95-based architectures are widely used for enterprise-control integration, practical deployment of energy-focused monitoring frameworks across large industrial environments remains insufficiently documented. This paper presents a hierarchical multilevel smart energy monitoring system developed for industrial energy management based on ISA-95 principles and OT-IT integration. The proposed architecture integrates field instrumentation, PLC/SCADA systems, Manufacturing Execution Systems (MES), and Enterprise Resource Planning (ERP) platforms into a unified energy monitoring environment. The framework was implemented across more than 15 manufacturing facilities and integrated approximately 1,476 electrical analyzers, 214 compressed-air flowmeters, 126 gas correctors, 73 water meters, and nearly 30,000 operational data streams. The architecture enabled centralized monitoring, plant-level benchmarking, utility-system integration, and enterprise-wide visibility of energy performance. Evaluation using energy datasets obtained from 21 glass manufacturing facilities revealed that furnace systems accounted for approximately 73% of total energy consumption, while furnace and waste heat recovery projects represented 34.7% of identified energy-saving potential. The integrated architecture enabled systematic identification of energy-intensive assets, elimination of fragmented data structures, and improved cross-functional energy performance evaluation. The study demonstrates how an ISA-95-based energy monitoring architecture can support industrial energy management through scalable data integration, hierarchical decision support, and enterprise-wide energy visibility.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00697-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189168","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":"Identifying people’s energy information needs through Energy Citizen Personas","authors":"Ajesh Kumar,&nbsp;Toni Kuronen,&nbsp;Bilal Naqvi,&nbsp;Salla Annala,&nbsp;Lasse Lensu,&nbsp;Martina Massari,&nbsp;Saveria Olga Murielle Boulanger,&nbsp;Annika Wolff","doi":"10.1186/s42162-026-00691-0","DOIUrl":"10.1186/s42162-026-00691-0","url":null,"abstract":"<div><p>Energy Citizenship refers to individuals and communities participating, sometimes begrudgingly, in adopting energy solutions, whether sustainable or not. When enacted positively, it advances the green energy transition. There are several pathways for citizens to participate in the green energy transition; however, participation should be voluntary and compatible with citizens’ life contexts. Energy information is vital in enabling people to get involved in the green energy transition. However, less is known about how the energy information needs of citizens vary with their lifestyle and context. This paper presents energy citizen personas as an artifact for energy citizenship contexts, using the Research in the Wild (RITW) approach. The research incorporates findings from a survey conducted across 16 European countries involving 9414 respondents. The survey responses were analyzed using a clustering algorithm and contextualized into ‘energy citizen personas’ representing varying circumstances, goals, and energy information needs of energy citizens.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149102448","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 dual-confirmation multivariate feature selection framework using partial lag correlation matrix function for multi-region electricity load forecasting","authors":"Yunita Ardilla,&nbsp;Ahmad Saikhu,&nbsp;Sutikno Sutikno","doi":"10.1186/s42162-026-00689-8","DOIUrl":"10.1186/s42162-026-00689-8","url":null,"abstract":"<div><p>Short-term electricity load forecasting in multi-region systems is a critical challenge in power grid management, particularly in heterogeneous interconnected systems with temporal dependencies between subsystems. While most deep learning studies for electricity load forecasting rely on heuristic sliding window approaches without any formal feature selection, the subset of studies that explicitly perform temporal feature selection has been predominantly limited to univariate Partial Autocorrelation Function (PACF), an approach inherently incapable of detecting cross-regional temporal dependencies once multivariate confounding effects are controlled for. This study proposes a multivariate temporal feature selection framework based on the Partial Lag Correlation Matrix Function (PLCMF), a formal multivariate generalization of PACF in a vector autoregression framework validated through the Granger Causality test as a dual-confirmation principle. Only cross-regional lags that simultaneously satisfy both conditions of direct partial dependency significance (PLCMF) and incremental predictive relevance (Granger) are integrated as input features of a gated recurrent unit model with a multi-input multi-output architecture. The evaluation results on the test data show that GRU-PLCMF outperforms GRU with a sliding window (GRU-SW) and GRU with univariate PACF (GRU-PACF) in 25 of the 54 evaluation scenarios across three distinct test windows, with a minimum MAPE value of 1.81%. The PLCMF analysis successfully identified partial dependency structures that varied systematically across periods, including cross-regional load compensation phenomena and asymmetric bidirectional dependency patterns, which could not be detected by univariate or sliding window approaches. These findings provide empirical evidence that the integration of a multivariate statistics-based temporal feature selection framework improves the out-of-sample forecasting accuracy of the GRU model and the interpretability of the cross-regional feature selection structure used for simultaneous multi-region electricity load forecasting.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00689-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132857","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":"Thematic alignment across expert-guided and machine-derived topic models in Positive Energy District characterization","authors":"Juveria Shah,&nbsp;Mengjie Han,&nbsp;Xingxing Zhang","doi":"10.1186/s42162-026-00688-9","DOIUrl":"10.1186/s42162-026-00688-9","url":null,"abstract":"<div><p>Positive Energy Districts (PEDs) are increasingly framed through both expert-driven policy narratives and data-driven analyses of emerging project discourse. However, the extent to which different knowledge extraction approaches produce conceptually meaningful representations of the PED concept remains insufficiently examined. This study examines thematic patterns in two representative English-language corpora using expert-guided and machine-derived topic modeling with a three-way analytical design. A Pseudo-document-Guided LDA (PG-LDA) model was applied to structured case descriptions from the PED-DB (66 documents after preprocessing), while unsupervised Latent Dirichlet Allocation (LDA) was applied to an unstructured corpus of 35 documents retained from 44 identified PED project sources (approximately 149,420 words). To provide a comparative reference point for interpreting differences attributable to modeling approach and corpus type, an additional unsupervised control LDA (K = 7) was trained on the same structured corpus as PG-LDA. Cross-corpus semantic alignment was evaluated using sentence-embedding cosine similarity (SBERT; all-MiniLM-L6-v2), which accommodates the near-zero lexical overlap between expert-defined bigram topics (PG-LDA) and corpus-derived unigram topics (LDA). Results indicate moderate semantic alignment across most thematic dimensions (SBERT cosine similarity range: 0.348–0.535), with the cross-model comparison (PG-LDA vs. control LDA; mean SBERT = 0.453) and the cross-corpus comparison (control LDA vs. unstructured LDA; mean SBERT = 0.462) showing broadly comparable alignment scores. Stakeholder integration showed consistently weak alignment across all model comparisons, while financial feasibility and mobility integration remained comparatively underrepresented in both corpora. These asymmetries suggest differences in how PED knowledge is produced, communicated, and institutionalized across expert-curated and project-communication contexts. The findings suggest that expert-guided modeling supports conceptual consolidation of policy-relevant themes, whereas machine-driven modeling reveals emergent narratives and contextual experimentation dynamics. By integrating deductive and inductive analytical perspectives, the study contributes to a more reflexive understanding of PED discourse and supports the development of more balanced knowledge frameworks with broader qualitative and institutional evidence.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00688-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928097","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":"The effect of efficiency and self-discharge parameterization on the sizing of hybrid energy storage systems","authors":"Sebastian Günther,&nbsp;Astrid Bensmann,&nbsp;Richard Hanke-Rauschenbach","doi":"10.1186/s42162-026-00676-z","DOIUrl":"10.1186/s42162-026-00676-z","url":null,"abstract":"<div><p>A hybrid energy storage system (HESS) is a widely investigated setup as a means to reduce losses and system dimensions by combining storage technologies with complementary characteristics. However, reported benefits are often based on case studies and do not clearly distinguish systemic operational effects from intrinsic parameter advantages. This work investigates under which conditions heterogeneous loss characteristics in a HESS lead to measurable loss savings and sizing gains compared to a single storage system. To eliminate parameterization bias, the concept of loss-equivalent storage units is employed. A linear storage model is formulated as a cascaded and bi-level quadratic programming problem that minimizes system dimensions under defined loss constraints. A parameter study is conducted across 1024 load profiles, varying efficiency, self-discharge rate, and storage dimensions, resulting in more than 800 000 parameter configurations. The dominant predictor of both sizing gains and loss savings is the variation coefficient of the load profile. Strong statistical relationships are observed (Pearson correlation coefficients <span>(approx)</span> 0.9). Nevertheless, practical sizing gains are limited and rarely exceed 3% within realistic parameter ranges. In contrast, relative loss savings are substantial and often exceed 20%, following a saturating functional relationship with sizing gains. The results indicate that load-profile variability primarily governs the benefits of a HESS with regards to loss reduction. While dimensional advantages are modest, loss reductions remain pronounced and relevant for operational cost and thermal management. Data and results are accessible via https://zenodo.org/records/18770498 and https://github.com/s-guenther/hess-study.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00676-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782861","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":"Machine learning-based uncertainty quantification for energy consumption and driving range estimation in electric cargo vehicles","authors":"Mrugank Gandhi,&nbsp;Archana Y. Chaudhari","doi":"10.1186/s42162-026-00682-1","DOIUrl":"10.1186/s42162-026-00682-1","url":null,"abstract":"<div><p>The energy consumption estimation and remaining driving range estimation are important aspects of electric vehicles for intelligent transportation systems, battery management systems, and for reducing range anxiety. Typical deterministic models, however, do not account for the uncertainty associated with dynamic driving behaviour, traffic variations, environmental conditions, and battery characteristics, and therefore have limited prediction ability. This study implements a comparative analysis of several uncertainty-aware machine learning models to estimate energy consumption and driving range of EVs using real-world driving data. The developed methods include SVR Residual Bootstrap, RF Variance Estimation, SVR Conformal Prediction, SVR Adaptive Conformal Prediction, LightGBM Quantile Regression, LightGBM Conformal Quantile Regression (CQR), and LightGBM Adaptive Conformal Quantile Regression (ACQR). The models are evaluated using deterministic metrics including RMSE, MAE, R², and uncertainty-specific metrics including PICP and MPIW. The experimental results showed that accuracy of predictions, uncertainty coverage reliability, and interval sharpness were not equally good for the frameworks evaluated, resulting in significant trade-offs. SVR Residual Bootstrap had the best prediction performance in a deterministic prediction with RMSE = 2.358, MAE = 1.742, and R² = 0.893. RF Variance and LightGBM CQR had higher uncertainty coverage but with wider intervals, while LightGBM ACQR had better balance between uncertainty coverage reliability and interval efficiency. Among all implemented approaches, the results of the SVR Conformal approach showed consistent and stable uncertainty estimation with reliability of the coverage (PICP = 0.95) and moderate interval width (MPIW = 15), making it suitable for real-world EV applications. The results underscore the significance of incorporating uncertainty quantification in EV prediction systems to ensure reliable and dependable sustainable transportation applications.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00682-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189804","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":"Decision intelligence for coupled mobility–energy infrastructures: learning to coordinate transportation and power systems","authors":"Yi Pan,&nbsp;Kemin Dai,&nbsp;Mingshen Wang,&nbsp;Xiaodong Yuan","doi":"10.1186/s42162-026-00643-8","DOIUrl":"10.1186/s42162-026-00643-8","url":null,"abstract":"<div><p>The rapid convergence of transportation electrification and urban energy systems introduces complex interdependencies among humans, electric vehicles, charging infrastructures, road networks, and distribution grids. This paper proposes a unified deep learning–based optimization framework for coordinated operation and resource allocation across the human–vehicle–charger–road–grid ecosystem. Unlike existing formulations relying on static optimization or distributionally robust approximations, the proposed method adopts a dynamic multi-agent deep reinforcement learning paradigm that jointly captures the spatiotemporal evolution of energy flow, traffic conditions, and user behaviors. Each agent−representing a distinct domain component−learns adaptive decision policies through cooperative reward sharing and environment embedding, enabling scalable coordination without centralized supervision. The mathematical formulation integrates a multi-term objective that minimizes total energy cost, travel delay, and grid imbalance, subject to nonlinear power–traffic coupling constraints. A hybrid convolutional–recurrent neural architecture encodes spatial topology and temporal dependencies, while federated gradient aggregation preserves cross-agent learning stability. Case studies based on a synthetic urban energy–mobility testbed demonstrate that the proposed framework reduces total system cost by 21.7%, improves renewable utilization by 24%, and enhances voltage stability by 15% compared with benchmark methods. The results further reveal robust adaptation to renewable forecast errors up to 20% and high-traffic scenarios with 30% demand surges. This study advances a generalized paradigm for cross-domain deep optimization, bridging the gap between intelligent mobility and resilient grid operation. The framework provides a scalable pathway toward data-driven, sustainable, and self-coordinating urban energy ecosystems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00643-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132962","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":"COF-Tool: a supervised method and a tool for building classification and disaggregation of electricity for heating from hourly electricity load profiles from buildings","authors":"Synne Krekling Lien,&nbsp;Jayaprakash Rajasekharan","doi":"10.1186/s42162-026-00681-2","DOIUrl":"10.1186/s42162-026-00681-2","url":null,"abstract":"<div><p>Data-driven methods can extract valuable information from building smart meter data. This article presents COF-Tool, a supervised methodological framework and open-source tool with trained models for classifying building categories and heating types, and for disaggregating heating electricity use directly from hourly electric smart meter data of buildings. The tool is trained and validated on real energy use measurements from Norwegian buildings with at least one year of data and consists of two modules: the classification module based on a random forest classifier, trained on data from 6403 buildings across 12 building categories, and the disaggregation module based on a Categorical Boosting (CB) regressor, trained and validated on data from 323 buildings across 9 building categories. To address the scarcity of labelled electric-heating data, the disaggregation module employs a cross-domain training approach where smart electricity and district heating measurements from buildings with district heating (DH) serve as a proxy for all-electric buildings. Results show that the cross-domain approach generalises to school and apartment buildings but is only tested on DH buildings for other categories. The effect of including classification probabilities and extended features in the disaggregation module is also evaluated. Classification performance is reduced compared to the original version, due to an increased number of residential units in the training set and common confusion between apartments and houses, as well as a reduced number of schools in the training set to avoid information leakage, as these buildings are reserved for the disaggregation training set. COF-Tool is publicly available as a GitHub repository and a web application, providing an accessible interface for extracting building information and heating electricity estimates from hourly smart meter data.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00681-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752109","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":"Assessing modeling choices for distributed energy resources in demand side management based on the Shapley value","authors":"Chang Li,&nbsp;Kevin Förderer,&nbsp;Jörg Matthes,&nbsp;Veit Hagenmeyer","doi":"10.1186/s42162-026-00680-3","DOIUrl":"10.1186/s42162-026-00680-3","url":null,"abstract":"<div><p>Distributed Energy Resources (DERs), i.e., small-scale electricity systems composed of local generators, storage systems and flexible demand, play a crucial role in Demand Side Management (DSM). In order for DSM measures to control a huge number of DERs in an optimal way, a balance between their model complexity and level of detail needs to be found. To deal with this challenge, we propose the evaluation of different white-box model variants, consisting of a different set of submodels, based on the Shapley value and the maximal power of the DER. The resulting Shapley value is used to assess which submodels contribute most to the model’s utility, which we measure by its accuracy. The approach is applied to a heat pump case study using a white-box modeling framework. In the modeling framework, four submodels are defined and assessed by utilizing the proposed approach. Their contributions are quantified based on the corresponding Shapley values, which are 10.58, 0.68, 0.25 and 0.52 <i>kW</i> respectively. Specifically, the contribution of the larger hot water storage (HWS), i.e. 0.52, is quantified as 2.08 times than that of the smaller HWS, i.e. 0.25, despite the larger HWS having only 1.39 times the storage capacity of the smaller HWS. It indicates that the contribution of the storage submodel is not linearly proportional to its physical capacity. The results demonstrate the feasibility of the proposed approach for white-box DER models and extend its applicability beyond black-box models in which it has primarily been studied. Therefore, this work provides a new perspective on utilizing the properties of the Shapley value to assess DER modeling choices.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00680-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465215","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":"Activating the electrical energy flexibility of residential thermal systems: an analysis of non-technical barriers in six countries","authors":"David Ritter,&nbsp;Christoph Rohringer,&nbsp;Alireza Afshari,&nbsp;Manuel Andrés-Chicote,&nbsp;Pablo Hernandez-Cruz,&nbsp;Kaya Duenzen,&nbsp;Dierk Bauknecht,&nbsp;Shady Attia","doi":"10.1186/s42162-026-00673-2","DOIUrl":"10.1186/s42162-026-00673-2","url":null,"abstract":"<div><p>Decentralised energy flexibility in electricity systems faces a range of non-technical barriers that constrain widespread implementation. This study focuses on flexibility from residential heat pumps (HP), especially in combination with thermally activated building systems (TABS). It provides a comparative analysis of regulatory, financial and stakeholder-related barriers in Austria, Belgium, Canada, Denmark, Germany and Spain. Besides these types of barriers, the cross-country analytical framework is structured around specific flexibility use cases and utilisation mechanisms, which are schemes and market structures through which end-users’ flexibility can be activated. The analysis is based on expert consultations and a systematic review of scientific literature, offering insights into the multi-dimensional nature of the identified barriers. The findings highlight a significant disconnect between the technological availability of flexibility from residential heating systems and implementation. The main identified barriers are perceived high initial costs in combination with uncertain return on investments, insufficient awareness among end-users and professionals, reinforced by the insufficiently adopted regulatory setting. Insufficient regulatory consideration was identified, particularly for TABS, shortcomings in current energy policy frameworks were observed.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s42162-026-00673-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323131","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}