{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:52:43Z","timestamp":1784134363229,"version":"3.55.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP19579354"],"award-info":[{"award-number":["AP19579354"]}]},{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP19579354"],"award-info":[{"award-number":["AP19579354"]}]},{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP19579354"],"award-info":[{"award-number":["AP19579354"]}]},{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"crossref","award":["EP\/V042955\/1"],"award-info":[{"award-number":["EP\/V042955\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"DOI":"10.1186\/s42162-025-00481-0","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T10:15:58Z","timestamp":1739182558000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hybrid feature-based neural network regression method for load profiles forecasting"],"prefix":"10.1186","volume":"8","author":[{"given":"Aidos","family":"Satan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurkhat","family":"Zhakiyev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aliya","family":"Nugumanova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Friedrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"481_CR1","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/0-387-23471-3_12","volume-title":"Applied Mathematics for Restructured Electric Power systems: optimization, control, and Computational Intelligence","author":"EA Feinberg","year":"2005","unstructured":"Feinberg EA, Genethliou D (2005) Load forecasting. In: Chow JH, Wu FF, Momoh JA (eds) Applied Mathematics for Restructured Electric Power systems: optimization, control, and Computational Intelligence. Springer, New York, p 269"},{"issue":"1","key":"481_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/00207720110067421","volume":"33","author":"HK Alfares","year":"2002","unstructured":"Alfares HK, Nazeeruddin M (2002) Electric load forecasting: literature survey and classification of methods. Int J Syst Sci 33(1):23\u201334","journal-title":"Int J Syst Sci"},{"key":"481_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.energy.2018.10.119","volume":"166","author":"Y Liang","year":"2019","unstructured":"Liang Y, Niu D, Wei-Chiang H (2019) Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 166:653\u2013663. https:\/\/doi.org\/10.1016\/j.energy.2018.10.119","journal-title":"Energy"},{"key":"481_CR4","doi-asserted-by":"publisher","unstructured":"Mihai C, Ilea D, Mircea PM (2016) Use of load profile curves for the energy market. In 2016 International Conference on Development and Application Systems (DAS), https:\/\/doi.org\/10.1109\/DAAS.2016.7492549","DOI":"10.1109\/DAAS.2016.7492549"},{"key":"481_CR5","unstructured":"Elexon (2018) Load profiles and their use in electricity settlement. Retrieved August 18, 2022 from https:\/\/www.elexon.co.uk\/documents\/training-guidance\/bsc-guidance-notes\/load-profiles\/"},{"key":"481_CR6","doi-asserted-by":"crossref","unstructured":"Chicco G (2016) Customer behaviour and data analytics. In International Conference and Exposition on Electrical and Power Engineering (EPE)","DOI":"10.1109\/ICEPE.2016.7781443"},{"key":"481_CR7","unstructured":"Ray GL, Pinson P (2019) Online adaptive clustering algorithm for load profiling. Sustainable Energy, Grids and Networks"},{"key":"481_CR8","unstructured":"Neagu B, Georgescu G (2011) and M. GU. Load curves characteristics of consumers supplied from electricity repartition and distribution public systems. Buletinul Institutului Politehnic din Iasi, Tomul LVII (LXI), 141\u2013157"},{"issue":"2","key":"481_CR9","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TPWRS.2012.2223240","volume":"28","author":"M Koivisto","year":"2013","unstructured":"Koivisto M, Heine P, Mellin I, Lehtonen M (2013) Clustering of connection points and load modeling in distribution systems. IEEE Trans Power Syst 28(2):1255\u20131265. https:\/\/doi.org\/10.1109\/TPWRS.2012.2223240","journal-title":"IEEE Trans Power Syst"},{"key":"481_CR10","doi-asserted-by":"publisher","unstructured":"Cross N, Gaunt CT (2003) Application of rural residential hourly load curves in energy modelling. In 2003 IEEE Bologna Power Tech Conference Proceedings, Vol.3, 4 pp.\u00a0https:\/\/doi.org\/10.1109\/PTC.2003.1304492","DOI":"10.1109\/PTC.2003.1304492"},{"key":"481_CR11","doi-asserted-by":"publisher","unstructured":"Chantelou D, Hebrail G, Muller C (1996) Visualizing 2665 electric power load curves on a single A4 sheet of paper. In Proceedings of the International Conference on Intelligent System Application to Power Systems, 126\u2013132. https:\/\/doi.org\/10.1109\/ISAP.1996.501056","DOI":"10.1109\/ISAP.1996.501056"},{"key":"481_CR12","unstructured":"Lo KL, Zakaria Z, Sohod MH (2005) Determination of consumers\u2019 load profiles based on two-stage fuzzy c-means. In Proceedings of the 5th WSEAS International Conference on Power Systems and Electromagnetic Compatibility, Greece, 212\u2013217"},{"key":"481_CR13","doi-asserted-by":"publisher","unstructured":"Gerbec D, Gubina F, Toros Z (2005) Actual load profiles of consumers without real time metering. In IEEE Power Engineering Society General Meeting, Vol. 3, 2578\u20132582. https:\/\/doi.org\/10.1109\/PES.2005.1489579","DOI":"10.1109\/PES.2005.1489579"},{"key":"481_CR14","doi-asserted-by":"publisher","unstructured":"Panapakidis IP, Alexiadis MC, Papagiannis GK (2012) Load profiling in the deregulated electricity markets: A review of the applications. In 2012 9th International Conference on the European Energy Market, 1\u20138. https:\/\/doi.org\/10.1109\/EEM.2012.6254762","DOI":"10.1109\/EEM.2012.6254762"},{"key":"481_CR15","unstructured":"Chen H, Canizares CA, Singh A (2001) ANN-based short-term load forecasting in electricity markets. In Proc. 2001 IEEE Power Engineering Society Winter Meeting, 414\u2013415"},{"key":"481_CR16","doi-asserted-by":"publisher","first-page":"1751","DOI":"10.1109\/TPWRS.2009.2038704","volume":"25","author":"D Fay","year":"2010","unstructured":"Fay D, Ringwood JV (2010) On the influence of weather forecast errors in short-term load forecasting models. IEEE Trans Power Syst 25:1751\u20131758","journal-title":"IEEE Trans Power Syst"},{"key":"481_CR17","first-page":"5","volume":"30","author":"C Kang","year":"2006","unstructured":"Kang C, Zhou A, Wang P, Zheng G, Liu Y (2006) Impact analysis of hourly weather factors in short-term load forecasting and its processing strategy (in Chinese). Power Syst Technol 30:7, 5\u201310","journal-title":"Power Syst Technol"},{"key":"481_CR18","first-page":"99","volume":"2014","author":"I Ben\u00edtez","year":"2014","unstructured":"Ben\u00edtez I, Quijano A, D\u00edez JL, Delgado I (2014) Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers. Electr Power Energy Syst 2014:99\u2013108","journal-title":"Electr Power Energy Syst"},{"issue":"1","key":"481_CR19","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1109\/TSG.2013.2278477","volume":"5","author":"J Kwac","year":"2014","unstructured":"Kwac J, Flora J, Rajagopal R (Jan. 2014) Household energy consumption segmentation using hourly data. IEEE Trans Smart Grid 5(1):420\u2013430. https:\/\/doi.org\/10.1109\/TSG.2013.2278477","journal-title":"IEEE Trans Smart Grid"},{"key":"481_CR20","doi-asserted-by":"publisher","unstructured":"Panapakidis IP, Dagoumas AS (2019) Load curves partitioning with the application of soft clustering algorithms, in Proc. 54th Int. Universities Power Engineering Conference (UPEC), pp. 1\u20136. https:\/\/doi.org\/10.1109\/UPEC.2019.8893610","DOI":"10.1109\/UPEC.2019.8893610"},{"key":"481_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.energy.2019.05.124","volume":"2019","author":"S Yilmaz","year":"2019","unstructured":"Yilmaz S, Chambers J, Patel MK (2019) Comparison of clustering approaches for domestic electricity load profile characterization: implications for demand side management. Energy 2019:1\u201312. https:\/\/doi.org\/10.1016\/j.energy.2019.05.124","journal-title":"Energy"},{"issue":"1","key":"481_CR22","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/TSG.2015.2409786","volume":"7","author":"S Haben","year":"2016","unstructured":"Haben S, Singleton C, Grindrod P (Jan. 2016) Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Trans Smart Grid 7(1):136\u2013144. https:\/\/doi.org\/10.1109\/TSG.2015.2409786","journal-title":"IEEE Trans Smart Grid"},{"key":"481_CR23","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s10618-005-0039-x","volume":"13","author":"X Wang","year":"2006","unstructured":"Wang X, Smith K, Hyndman R (2006) Characteristic-based clustering for time series data. Data Min Knowl Discov 13:223\u2013252","journal-title":"Data Min Knowl Discov"},{"key":"481_CR24","doi-asserted-by":"crossref","unstructured":"Salam A, El Hibaoui A (2018) Comparison of machine learning algorithms for power consumption prediction: Case study of Tetouan city, in Proc. 6th Int. Renewable and Sustainable Energy Conf. (IRSEC), pp. 1\u20135","DOI":"10.1109\/IRSEC.2018.8703007"},{"key":"481_CR25","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.matcom.2021.05.006","volume":"190","author":"A Salam","year":"2021","unstructured":"Salam A A., El Hibaoui (2021) Energy consumption prediction model with deep inception residual network inspiration and LSTM. Math Comput Simul 190:97\u2013109","journal-title":"Math Comput Simul"},{"key":"481_CR26","doi-asserted-by":"publisher","unstructured":"He Y, Zhu Y, Chen Z (2019) Electricity consumption probability density forecasting method based on LASSO-Quantile regression neural network. Appl Energy, vVol. 233\u2013234, pp. 565\u2013575He Y., Qin Y, Wang S, Wang X, & Wang C (2019). Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network. Applied Energy, 233, 565-575. https:\/\/doi.org\/10.1016\/j.apenergy.2018.10.061","DOI":"10.1016\/j.apenergy.2018.10.061"},{"issue":"1","key":"481_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s43067-019-0008-x","volume":"7","author":"IK Nti","year":"2020","unstructured":"Nti IK, Adekoya SM, Nyarko-Boateng AK (2020) Electricity load forecasting: a systematic review. J Electr Syst Inform Technol 7(1):1\u201319","journal-title":"J Electr Syst Inform Technol"},{"key":"481_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/en16031434","author":"N Bacanin","year":"2023","unstructured":"Bacanin N, Tuba T, Zivkovic M, Sekulic M, Djurasevic M (2023) On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting. Energies. https:\/\/doi.org\/10.3390\/en16031434","journal-title":"Energies"},{"key":"481_CR29","doi-asserted-by":"publisher","first-page":"2574","DOI":"10.3390\/en16062574","volume":"16","author":"R Olu-Ajayi","year":"2023","unstructured":"Olu-Ajayi R, Melville S, Petridis S, Mahmood A (2023) Data-driven tools for building energy consumption prediction: A review. Energies 16:2574","journal-title":"Energies"},{"key":"481_CR30","doi-asserted-by":"crossref","unstructured":"Klemenjak C, Kovatsch C, Herold M, Elmenreich W (2019) Electricity consumption data sets: Pitfalls and opportunities, In Proc. 6th ACM Int. Conf. on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 159\u2013162","DOI":"10.1145\/3360322.3360867"},{"issue":"1","key":"481_CR31","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/s41597-020-0434-6","volume":"7","author":"C Klemenjak","year":"2020","unstructured":"Klemenjak C, Reinhardt A, Pereira L, Makonin S, Berg\u00e9s M, Elmenreich W (2020) A synthetic energy dataset for non-intrusive load monitoring in households. Sci Data 7(1):108","journal-title":"Sci Data"},{"key":"481_CR32","first-page":"90","volume":"189","author":"B Yildiz","year":"2017","unstructured":"Yildiz B, Bilbao JI, Dore J, Sproul AB (2017) Recent advances in the analysis of residential electricity consumption and applications of smart meter data. Appl Energy 189:90\u2013105","journal-title":"Appl Energy"},{"key":"481_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2011.2167524","author":"T Zhang","year":"2012","unstructured":"Zhang T, Zhang G, Lu J, Feng X, Yang W (2012) A new index and classification approach for load pattern analysis of large electricity customers. IEEE Transactions on Power Systems. https:\/\/doi.org\/10.1109\/TPWRS.2011.2167524","journal-title":"IEEE Transactions on Power Systems"},{"key":"481_CR34","doi-asserted-by":"publisher","unstructured":"Gupta T, Panda SP (2019) Clustering validation of CLARA and K-means using silhouette & DUNN measures on iris dataset, in Proc. 2019 Int. Conf. on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 10\u201313. https:\/\/doi.org\/10.1109\/COMITCon.2019.8862199","DOI":"10.1109\/COMITCon.2019.8862199"},{"key":"481_CR35","unstructured":"L. v. d. Maaten, Visualizing data using t-SNE, Journal of Machine Learning Research, vol. 9, pp. 2579\u20132605, Nov. (2008)"},{"key":"481_CR36","doi-asserted-by":"publisher","unstructured":"Shi D, Li R, Shi R, Li F (2014) Analysis of the relationship between load profile and weather condition, in Proc. 2014 IEEE PES General Meeting| Conf. & Exposition, pp. 1\u20135. https:\/\/doi.org\/10.1109\/PESGM.2014.6939855","DOI":"10.1109\/PESGM.2014.6939855"},{"key":"481_CR37","doi-asserted-by":"publisher","unstructured":"Satan A, Khamzina A, Toktarbayev D, Sotsial Z, Bapiyev I, Zhakiyev N (2022) Comparative LSTM and SVM machine learning approaches for energy consumption prediction: Case study in Akmola, In Proc. 2022 Int. Conf. on Smart Information Systems and Technologies (SIST), pp. 1\u20137. https:\/\/doi.org\/10.1109\/SIST54437.2022.9945776","DOI":"10.1109\/SIST54437.2022.9945776"},{"key":"481_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2023.100241","author":"F Gallego","year":"2023","unstructured":"Gallego F, Mart\u00edn C, D\u00edaz M, Garrido D (2023), article 100241, Maintaining flexibility in smart grid consumption through deep learning and deep reinforcement learning. Energy AI 13. https:\/\/doi.org\/10.1016\/j.egyai.2023.100241","journal-title":"Energy AI"},{"issue":"4","key":"481_CR39","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1109\/TPWRS.2003.811172","volume":"18","author":"S Ru\u017eic","year":"2003","unstructured":"Ru\u017eic S, Vuckovic A, Nikolic N (2003) Weather sensitive method for short term load forecasting in electric power utility of Serbia. IEEE Transactions on Power Systems 18(4):1581\u20131586","journal-title":"IEEE Transactions on Power Systems"},{"key":"481_CR40","doi-asserted-by":"crossref","unstructured":"Moghaddas-Tafreshi SM, Farhadi M (2008) A linear regression-based study for temperature sensitivity analysis of Iran electrical load, In Proc. 2008 IEEE Int. Conf. on Industrial Technology, pp. 1\u20137","DOI":"10.1109\/ICIT.2008.4608590"},{"key":"481_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2020.100028","author":"S Chen","year":"2020","unstructured":"Chen S, Ren Y, Friedrich D, Yu Z, Yu J (2020), article 100028, Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction. Energy AI 2. https:\/\/doi.org\/10.1016\/j.egyai.2020.100028","journal-title":"Energy AI"},{"key":"481_CR42","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.apenergy.2019.01.022","volume":"237","author":"L Xu","year":"2019","unstructured":"Xu L, Wang S, Tang R (2019) Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. Appl Energy 237:180\u2013195","journal-title":"Appl Energy"},{"key":"481_CR43","doi-asserted-by":"publisher","DOI":"10.3390\/math11122786","author":"K Wang","year":"2023","unstructured":"Wang K, Du H, Wang J, Jia R, Zong Z (2023) An ensemble deep learning model for provincial load forecasting based on reduced dimensional clustering and decomposition strategies. Mathematics. https:\/\/doi.org\/10.3390\/math11122786","journal-title":"Mathematics"},{"issue":"Suppl 1","key":"481_CR44","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s42162-022-00212-9","volume":"5","author":"L Semmelmann","year":"2022","unstructured":"Semmelmann L, Henni S, Weinhardt C (2022) Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data. Energy Inf 5(Suppl 1):24. https:\/\/doi.org\/10.1186\/s42162-022-00212-9","journal-title":"Energy Inf"},{"key":"481_CR45","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s42162-023-00299-8","volume":"6","author":"O Neumann","year":"2023","unstructured":"Neumann O, Turowski M, Mikut R et al (2023) Using weather data in energy time series forecasting: the benefit of input data transformations. Energy Inf 6:44. https:\/\/doi.org\/10.1186\/s42162-023-00299-8","journal-title":"Energy Inf"},{"key":"481_CR46","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.procs.2024.08.069","volume":"241","author":"N Zhakiyev","year":"2024","unstructured":"Zhakiyev N, Satan A, Akhmetkanova G, Medeshova A, Omirgaliyev R, Bracco S (2024) Energy Management System for the campus Microgrid using an internet of things as a service (IoTaaS) with day-ahead forecasting. Procedia Comput Sci 241:488\u2013493. https:\/\/doi.org\/10.1016\/j.procs.2024.08.069","journal-title":"Procedia Comput Sci"},{"key":"481_CR47","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1038\/s42256-021-00302-5","volume":"3","author":"L Lu","year":"2021","unstructured":"Lu L, Jin P, Pang G et al (2021) Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat Mach Intell 3:218\u2013229. https:\/\/doi.org\/10.1038\/s42256-021-00302-5","journal-title":"Nat Mach Intell"},{"key":"481_CR48","doi-asserted-by":"publisher","DOI":"10.37943\/16YIKA8050","author":"S Kabdygali","year":"2023","unstructured":"Kabdygali S, Omirgaliyev R, Tursynbayev T, Kayisli K, Zhakiyev N (2023) Deep recurrent neural networks in energy demand forecasting: a case study of Kazakhstan\u2019s electrical consumption. Sci J Astana IT Univ. https:\/\/doi.org\/10.37943\/16YIKA8050","journal-title":"Sci J Astana IT Univ"},{"key":"481_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21689-3_45","volume-title":"Intelligent systems. BRACIS 2022","author":"WL Vital","year":"2022","unstructured":"Vital WL, Vieira G, Valle ME (2022) Extending the Universal Approximation Theorem for a broad class of hypercomplex-valued neural networks. In: Xavier-Junior JC, Rios RA (eds) Intelligent systems. BRACIS 2022. Lecture Notes in Computer Science, vol 13654. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-21689-3_45"},{"key":"481_CR50","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation. https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"481_CR51","doi-asserted-by":"publisher","DOI":"10.3390\/rs16111870","author":"VO Santos","year":"2024","unstructured":"Santos VO, Guimar\u00e3es BMDM, Neto IEL, de Souza Filho F, Rocha PAC, Th\u00e9 JVG, Gharabaghi B (2024) Chlorophyll-a estimation in 149 tropical semi-arid reservoirs using remote sensing data and six machine learning methods. Remote Sensing. https:\/\/doi.org\/10.3390\/rs16111870","journal-title":"Remote Sensing"},{"key":"481_CR52","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00302-5","author":"L Lu","year":"2021","unstructured":"Lu L, Jin P, Pang G, Zhang Z, Karniadakis GE (2021) Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat Mach Intell. https:\/\/doi.org\/10.1038\/s42256-021-00302-5","journal-title":"Nat Mach Intell"},{"key":"481_CR53","doi-asserted-by":"publisher","unstructured":"Sagadatova N, Talas B, Nugumanova A, Zhakiyev N (2023) Forecasting Electricity Consumption: Case Study in Astana. Sci J Astana IT Univ. 14. https:\/\/doi.org\/10.37943\/14TRMF1662","DOI":"10.37943\/14TRMF1662"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-025-00481-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42162-025-00481-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-025-00481-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T10:16:13Z","timestamp":1739182573000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-025-00481-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["481"],"URL":"https:\/\/doi.org\/10.1186\/s42162-025-00481-0","relation":{},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]},"assertion":[{"value":"17 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"19"}}