{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:11:03Z","timestamp":1743826263275,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887192","type":"print"},{"value":"9783031887208","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-88720-8_25","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:03:34Z","timestamp":1743768214000},"page":"151-156","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging LLMs for\u00a0Energy Forecasting: The AcegasApsAmga Case Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9191-3280","authenticated-orcid":false,"given":"Kevin","family":"Roitero","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1465-6521","authenticated-orcid":false,"given":"Andrea","family":"Zancola","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0144-3802","authenticated-orcid":false,"given":"Vincenzo","family":"Della Mea","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2852-168X","authenticated-orcid":false,"given":"Stefano","family":"Mizzaro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 09, pp. 41\u201348. Association for Computing Machinery, New York, NY, USA (2009). https:\/\/doi.org\/10.1145\/1553374.1553380","DOI":"10.1145\/1553374.1553380"},{"key":"25_CR2","doi-asserted-by":"publisher","unstructured":"Chaturvedi, S., Rajasekar, E., Natarajan, S., McCullen, N.: A comparative assessment of sarima, lstm rnn and fb prophet models to forecast total and peak monthly energy demand for India. Energy Policy 168, 113097 (2022). https:\/\/doi.org\/10.1016\/j.enpol.2022.113097, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0301421522003226","DOI":"10.1016\/j.enpol.2022.113097"},{"key":"25_CR3","doi-asserted-by":"crossref","first-page":"100456","DOI":"10.1016\/j.imu.2020.100456","volume":"21","author":"V Della Mea","year":"2020","unstructured":"Della Mea, V., Popescu, M.H., Roitero, K.: Underlying cause of death identification from death certificates using reverse coding to text and a nlp based deep learning approach. Inf. Med. Unlocked 21, 100456 (2020)","journal-title":"Inf. Med. Unlocked"},{"key":"25_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"1","key":"25_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/for.3980040103","volume":"4","author":"ES Gardner Jr","year":"1985","unstructured":"Gardner, E.S., Jr.: Exponential smoothing: the state of the art. J. Forecast. 4(1), 1\u201328 (1985)","journal-title":"J. Forecast."},{"key":"25_CR6","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 9459\u20139474. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/6b493230205f780e1bc26945df7481e5-Paper.pdf"},{"issue":"4","key":"25_CR7","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim, B., Ar\u0131k, S.\u00d6., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 1748\u20131764 (2021)","journal-title":"Int. J. Forecast."},{"key":"25_CR8","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437 (2019)"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Permanasari, A.E., Hidayah, I., Bustoni, I.A.: Sarima (seasonal arima) implementation on time series to forecast the number of malaria incidence. In: 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 203\u2013207 (2013). https:\/\/doi.org\/10.1109\/ICITEED.2013.6676239","DOI":"10.1109\/ICITEED.2013.6676239"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Popescu, M.H., Roitero, K., Travasci, S., Della\u00a0Mea, V.: Automatic assignment of ICD-10 codes to diagnostic texts using transformers based techniques. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 188\u2013192. IEEE (2021)","DOI":"10.1109\/ICHI52183.2021.00037"},{"issue":"140","key":"25_CR11","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Roitero, K., D\u2019Abrosca, G., Zancola, A., Della\u00a0Mea, V., Mizzaro, S.: Generative AI for energy: multi-horizon power consumption forecasting using large language models. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, pp. 4015-4019. Association for Computing Machinery, New York, NY, USA (2024). https:\/\/doi.org\/10.1145\/3627673.3679933","DOI":"10.1145\/3627673.3679933"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Roitero, K., et al.: Detection of wastewater pollution through natural language generation with a low-cost sensing platform. IEEE Access 11, 50272\u201350284 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3277535","DOI":"10.1109\/ACCESS.2023.3277535"},{"issue":"3","key":"25_CR14","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: Deepar: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181\u20131191 (2020)","journal-title":"Int. J. Forecast."},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Shumway, R.H., Stoffer, D.S.: ARIMA Models, pp. 75\u2013163. Springer International Publishing, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-52452-8_3","DOI":"10.1007\/978-3-319-52452-8_3"},{"key":"25_CR16","unstructured":"Wang, S., Li, C., Lim, A.: Why are the arima and sarima not sufficient. arXiv preprint arXiv:1904.07632 (2019)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Xue, H., Tang, T., Payani, A., Salim, F.D.: Prompt mining for language-based human mobility forecasting. arXiv preprint arXiv:2403.03544 (2024)","DOI":"10.1145\/3678717.3691232"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Xue, H., Voutharoj, B.P., Salim, F.D.: Leveraging language foundation models for human mobility forecasting. arXiv preprint arXiv:2209.05479 (2022)","DOI":"10.1145\/3557915.3561026"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88720-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:03:50Z","timestamp":1743768230000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88720-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887192","9783031887208"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88720-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}