{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T23:09:12Z","timestamp":1778368152871,"version":"3.51.4"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032191014","type":"print"},{"value":"9783032191021","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-19102-1_3","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:17:27Z","timestamp":1778365047000},"page":"42-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Real-World Deployment of\u00a0Federated Learning for\u00a0Residential Solar PV Power Forecasting"],"prefix":"10.1007","author":[{"given":"Frederick","family":"Apina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diogo","family":"Monteiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bruno","family":"Dias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo","family":"Morais","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucas","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"3_CR1","unstructured":"Al-Shorbaji, A., et\u00a0al.: Data sharing, privacy and security considerations in the energy sector: A review from technical landscape to regulatory specifics. arXiv preprint arXiv:2403.02476 (2024)"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.solener.2016.06.069","volume":"136","author":"J Antonanzas","year":"2016","unstructured":"Antonanzas, J., et al.: Review of solar power forecasting. Sol. Energy 136, 78\u2013109 (2016)","journal-title":"Sol. Energy"},{"key":"3_CR3","unstructured":"Ariff, M.F.M., et\u00a0al.: Advancing power system services with privacy-preserving federated learning techniques: a review. UPCommons (2024). https:\/\/upcommons.upc.edu\/handle\/2117\/402809"},{"key":"3_CR4","unstructured":"Beutel, D.J., et al.: Flower: A friendly federated learning research framework, arXiv preprint arXiv:2007.14390 (2020)"},{"key":"3_CR5","unstructured":"Copernicus Atmosphere Monitoring Service (CAMS): CAMS solar radiation time-series (2020). https:\/\/ads.atmosphere.copernicus.eu\/datasets\/cams-solar-radiation-timeseries, Accessed 04 Jan 2025"},{"key":"3_CR6","unstructured":"EnergyREV Consortium: Privacy and data sharing in smart local energy systems: insights and recommendations. Technical report EnergyREV (2020). https:\/\/www.energyrev.org.uk\/media\/1586\/privacy-and-data-sharing-in-sles.pdf"},{"issue":"3","key":"3_CR7","doi-asserted-by":"publisher","first-page":"2440","DOI":"10.1109\/TSG.2022.3148699","volume":"13","author":"A Faustine","year":"2022","unstructured":"Faustine, A., Pereira, L.: Fpseq2q: fully parameterized sequence to quantile regression for net-load forecasting with uncertainty estimates. IEEE Trans. Smart Grid 13(3), 2440\u20132451 (2022). https:\/\/doi.org\/10.1109\/TSG.2022.3148699","journal-title":"IEEE Trans. Smart Grid"},{"issue":"1\u20132","key":"3_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"A Kairouz","year":"2021","unstructured":"Kairouz, A., et al.: Advances and open problems in federated learning. Found, Trends Mach. Learn. 14(1\u20132), 1\u2013210 (2021)","journal-title":"Found, Trends Mach. Learn."},{"issue":"3","key":"3_CR9","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1007\/s13369-019-04183-0","volume":"45","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Rizwan, M., Nangia, U.: A hybrid intelligent approach for solar photovoltaic power forecasting: impact of aerosol data. Arab. J. Sci. Eng. 45(3), 1715\u20131732 (2020). https:\/\/doi.org\/10.1007\/s13369-019-04183-0","journal-title":"Arab. J. Sci. Eng."},{"key":"3_CR10","volume":"322","author":"H Lee","year":"2022","unstructured":"Lee, H., Yoon, J., Choi, S.: Fedccl: federated learning with clustered client learning for solar power forecasting. Appl. Energy 322, 119492 (2022)","journal-title":"Appl. Energy"},{"key":"3_CR11","unstructured":"McMahan, H.B., et\u00a0al.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of AISTATS (2017)"},{"key":"3_CR12","unstructured":"Misra, S., Ojha, T., Sakor, A.: Federated learning for distributed load forecasting: Addressing data imbalance in smart grids. IEEE Trans. Ind. Inf. (2025)"},{"issue":"10","key":"3_CR13","doi-asserted-by":"crossref","first-page":"4887","DOI":"10.3390\/app12104887","volume":"12","author":"L Nespoli","year":"2022","unstructured":"Nespoli, L., et al.: A deep learning-based model for photovoltaic power forecasting. Appl. Sci. 12(10), 4887 (2022)","journal-title":"Appl. Sci."},{"key":"3_CR14","unstructured":"Open-Meteo: Open-Meteo weather forecast api, https:\/\/open-meteo.com\/, Accessed 10 Feb 2025"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Pereira, L., Nair, V., Annaswamy, A., Dias, B., Morais, H.: Accurate federated learning with uncertainty quantification for distributed energy resource forecasting applied to smart grids planning and operation: the alamo vision. In: IET Conference Proceedings, vol.\u00a02024, pp. 1123\u20131126 (2024)","DOI":"10.1049\/icp.2024.1930"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Pereira, L., Monteiro, D., Apina, F.: Supplementary Material for: A Real-World Deployment of Federated Learning for Residential Solar PV Power Forecasting (2025). https:\/\/doi.org\/10.17605\/OSF.IO\/J6NG5","DOI":"10.17605\/OSF.IO\/J6NG5"},{"key":"3_CR17","unstructured":"Prsma, M-ITI, EEM, ACIF-CCIM: Installation report of dsm demo (m36 version). Tech. Rep.\u00a04.7, EUROPEAN COMMISSION, Funchal, Portugal (2020). https:\/\/ec.europa.eu\/research\/participants\/documents\/downloadPublic?documentIds=080166e5cebf3967&appId=PPGMS"},{"issue":"18","key":"3_CR18","doi-asserted-by":"publisher","first-page":"5786","DOI":"10.3390\/en14185786","volume":"14","author":"F Quintal","year":"2021","unstructured":"Quintal, F., Garigali, D., Vasconcelos, D., Cavaleiro, J., Santos, W., Pereira, L.: Energy monitoring in the wild: platform development and lessons learned from a real-world demonstrator. Energies 14(18), 5786 (2021). https:\/\/doi.org\/10.3390\/en14185786","journal-title":"Energies"},{"issue":"2","key":"3_CR19","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/TSG.2023.3310979","volume":"15","author":"T Riedel","year":"2024","unstructured":"Riedel, T., Falkner, M., Fischer, A., Moslehi, K.: Residential load and pv feed-in forecasting using federated learning. IEEE Transactions on Smart Grid 15(2), 1450\u20131462 (2024)","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"1","key":"3_CR20","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N., et al.: The future of digital health with federated learning. NPJ Digital Med. 3(1), 119 (2020)","journal-title":"NPJ Digital Med."},{"key":"3_CR21","unstructured":"Savio, D.A., et\u00a0al.: Federated learning for the power grid: a survey. arXiv preprint arXiv:2303.12093 (2023)"},{"issue":"4","key":"3_CR22","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.3390\/en14041081","volume":"14","author":"S Theocharides","year":"2021","unstructured":"Theocharides, S., Theristis, M., Makrides, G., Kynigos, M., Spanias, C., Georghiou, G.E.: Comparative analysis of machine learning models for day-ahead photovoltaic power production forecasting. Energies 14(4), 1081 (2021). https:\/\/doi.org\/10.3390\/en14041081","journal-title":"Energies"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19102-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:17:29Z","timestamp":1778365049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19102-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191014","9783032191021"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19102-1_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","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":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}