{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T21:21:13Z","timestamp":1758057673071,"version":"3.44.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051783","type":"print"},{"value":"9783032051790","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05179-0_36","type":"book-chapter","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T21:43:29Z","timestamp":1757972609000},"page":"477-490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Retraining Framework for\u00a0Reliable Energy Forecasting in\u00a0Smart Buildings"],"prefix":"10.1007","author":[{"given":"Let\u00edcia","family":"Gomes","sequence":"first","affiliation":[]},{"given":"Brigida","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Zita","family":"Vale","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1016\/j.egyr.2024.12.078","volume":"13","author":"A Azeem","year":"2025","unstructured":"Azeem, A., et al.: Mitigating concept drift challenges in evolving smart grids: an adaptive ensemble LSTM for enhanced load forecasting. Energy Rep. 13, 1369\u20131383 (2025). https:\/\/doi.org\/10.1016\/j.egyr.2024.12.078","journal-title":"Energy Rep."},{"key":"36_CR2","doi-asserted-by":"publisher","first-page":"108632","DOI":"10.1016\/j.knosys.2022.108632","volume":"245","author":"F Bayram","year":"2022","unstructured":"Bayram, F., Ahmed, B.S., Kassler, A.: From concept drift to model degradation: an overview on performance-aware drift detectors. Knowl.-Based Syst. 245, 108632 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.108632","journal-title":"Knowl.-Based Syst."},{"key":"36_CR3","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"36_CR4","doi-asserted-by":"publisher","first-page":"114953","DOI":"10.1016\/j.enbuild.2024.114953","volume":"324","author":"R Costa","year":"2024","unstructured":"Costa, R., Silva, R., Faia, R., Gomes, L., Faria, P., Vale, Z.: Empowering energy management in smart buildings: a comprehensive study on distributed energy storage systems for Sustainable consumption. Energy Build. 324, 114953 (2024). https:\/\/doi.org\/10.1016\/j.enbuild.2024.114953","journal-title":"Energy Build."},{"key":"36_CR5","doi-asserted-by":"publisher","first-page":"101624","DOI":"10.1016\/j.segan.2025.101624","volume":"41","author":"R Faia","year":"2025","unstructured":"Faia, R., Faria, P., Vale, Z.: Optimal energy management with discomfort calculation in residential buildings considering load shifting and home battery storage system. Sustain. Energy Grids Netw. 41, 101624 (2025). https:\/\/doi.org\/10.1016\/j.segan.2025.101624","journal-title":"Sustain. Energy Grids Netw."},{"key":"36_CR6","doi-asserted-by":"publisher","unstructured":"Gailhofer, P., et\u00a0al.: The role of artificial intelligence in the European green deal (2021). https:\/\/doi.org\/10.13140\/RG.2.2.26789.22244","DOI":"10.13140\/RG.2.2.26789.22244"},{"key":"36_CR7","doi-asserted-by":"publisher","first-page":"110324","DOI":"10.1016\/j.engappai.2025.110324","volume":"147","author":"A Gonz\u00e1lez-Briones","year":"2025","unstructured":"Gonz\u00e1lez-Briones, A., et al.: Evolution of building energy management systems for greater sustainability through explainable artificial intelligence models. Eng. Appl. Artif. Intell. 147, 110324 (2025). https:\/\/doi.org\/10.1016\/j.engappai.2025.110324","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1616","key":"36_CR8","doi-asserted-by":"publisher","first-page":"4873","DOI":"10.3390\/en14164873","volume":"14","author":"R Lazdins","year":"2021","unstructured":"Lazdins, R., Mutule, A., Zalostiba, D.: PV energy communities\u2013challenges and barriers from a consumer perspective: a literature review. Energies 14(1616), 4873 (2021). https:\/\/doi.org\/10.3390\/en14164873","journal-title":"Energies"},{"key":"36_CR9","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030, pp. 4768\u20134777. Curran Associates, Inc. (2017)"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"111610","DOI":"10.1016\/j.knosys.2024.111610","volume":"293","author":"A Mahadevan","year":"2024","unstructured":"Mahadevan, A., Mathioudakis, M.: Cost-aware retraining for machine learning. Knowl.-Based Syst. 293, 111610 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.111610","journal-title":"Knowl.-Based Syst."},{"key":"36_CR11","doi-asserted-by":"publisher","first-page":"101246","DOI":"10.1016\/j.aei.2021.101246","volume":"47","author":"K Ng","year":"2021","unstructured":"Ng, K., Chen, C.H., Lee, C., Jiao, J., Yang, Z.X.: A systematic literature review on intelligent automation: aligning concepts from theory, practice, and future perspectives. Adv. Eng. Inform. 47, 101246 (2021). https:\/\/doi.org\/10.1016\/j.aei.2021.101246","journal-title":"Adv. Eng. Inform."},{"key":"36_CR12","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR13","doi-asserted-by":"publisher","unstructured":"Pham, T.M.T., Premkumar, K., Naili, M., Yang, J.: Time to retrain? Detecting concept drifts in machine learning systems (2024). https:\/\/doi.org\/10.48550\/arXiv.2410.09190","DOI":"10.48550\/arXiv.2410.09190"},{"key":"36_CR14","doi-asserted-by":"publisher","unstructured":"Ramos, D., Faria, P., Gomes, L., Vale, Z.: Energy forecast in buildings addressing computation consumption in a green computing approach, pp.\u00a01\u20136 (2022). https:\/\/doi.org\/10.1109\/EEEIC\/ICPSEurope54979.2022.9854723","DOI":"10.1109\/EEEIC\/ICPSEurope54979.2022.9854723"},{"key":"36_CR15","doi-asserted-by":"publisher","first-page":"102190","DOI":"10.1016\/j.softx.2025.102190","volume":"30","author":"B Ribeiro","year":"2025","unstructured":"Ribeiro, B., Dias, D., Gomes, L., Vale, Z.: PEAK: python-based framework for heterogeneous agent communities. SoftwareX 30, 102190 (2025). https:\/\/doi.org\/10.1016\/j.softx.2025.102190","journal-title":"SoftwareX"},{"key":"36_CR16","doi-asserted-by":"publisher","first-page":"100230","DOI":"10.1016\/j.dajour.2023.100230","volume":"7","author":"A Saranya","year":"2023","unstructured":"Saranya, A., Subhashini, R.: A systematic review of explainable artificial intelligence models and applications: recent developments and future trends. Decis. Anal. J. 7, 100230 (2023). https:\/\/doi.org\/10.1016\/j.dajour.2023.100230","journal-title":"Decis. Anal. J."},{"key":"36_CR17","doi-asserted-by":"publisher","unstructured":"Teixeira, B., Valina, L., Pinto, T., Reis, A., Barroso, J., Vale, Z.: Exploring clustering to improve interpretability in complex energy forecasting models. In: 2024 International Conference on Smart Energy Systems and Technologies (SEST), pp.\u00a01\u20136 (2024). https:\/\/doi.org\/10.1109\/SEST61601.2024.10694413","DOI":"10.1109\/SEST61601.2024.10694413"},{"issue":"3","key":"36_CR18","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1038\/s41578-022-00490-5","volume":"8","author":"Z Yao","year":"2023","unstructured":"Yao, Z., et al.: Machine learning for a sustainable energy future. Nat. Rev. Mater. 8(3), 202\u2013215 (2023). https:\/\/doi.org\/10.1038\/s41578-022-00490-5","journal-title":"Nat. Rev. Mater."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05179-0_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T21:43:30Z","timestamp":1757972610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05179-0_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9783032051783","9783032051790"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05179-0_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"16 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2025.ualg.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}