{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:03:56Z","timestamp":1742922236338,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031300462"},{"type":"electronic","value":"9783031300479"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-30047-9_35","type":"book-chapter","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T09:06:06Z","timestamp":1680253566000},"page":"446-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forecasting Electricity Prices: An Optimize Then Predict-Based Approach"],"prefix":"10.1007","author":[{"given":"L\u00e9onard","family":"Tschora","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erwan","family":"Pierre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Plantevit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9line","family":"Robardet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,1]]},"reference":[{"key":"35_CR1","unstructured":"Cheng, H.-Y., Kuo, P.-H., Shen, Y., Huang, C.-J.: Deep convolutional neural network model for short-term electricity price forecasting (2020)"},{"key":"35_CR2","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1198\/073500102753410444","volume":"20","author":"F Diebold","year":"1992","unstructured":"Diebold, F., Mariano, R.: Comparing predictive accuracy. J. Bus. Econ. Stat. 20, 134\u2013144 (1992)","journal-title":"J. Bus. Econ. Stat."},{"key":"35_CR3","unstructured":"El Balghiti, O., Elmachtoub, A. N., Grigas, P., Tewari, A.: Generalization bounds in the predict-then-optimize framework. In: Neurips, vol. 32 (2019)"},{"key":"35_CR4","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1007\/978-3-030-44038-1_108","volume-title":"Web, Artificial Intelligence and Network Applications","author":"ZA Khan","year":"2020","unstructured":"Khan, Z.A., et al.: Short term electricity price forecasting through convolutional neural network (CNN). In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) WAINA 2020. AISC, vol. 1150, pp. 1181\u20131188. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44038-1_108"},{"key":"35_CR5","unstructured":"Krizhevsky, A. Sutskever, L., Hinto, G.E.: Imagenet classification with deep CNNs. Technical report, University of Toronto (2012)"},{"key":"35_CR6","doi-asserted-by":"publisher","first-page":"116983","DOI":"10.1016\/j.apenergy.2021.116983","volume":"293","author":"J Lago","year":"2021","unstructured":"Lago, J., Marcjasz, G., De Schutter, B., Weron, R.: Forecasting day-ahead electricity prices: a review of state-of-the-art algorithms, best practices and an open-access benchmark. Appl. Energy 293, 116983 (2021)","journal-title":"Appl. Energy"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Lago, J., Ridder, F.D., Schutter, B. D.: Forecasting day-ahead electricity prices deep learning approaches and empirical comparison of traditional algorithms. Technical report, Delft University of Technology (2018)","DOI":"10.1016\/j.apenergy.2018.02.069"},{"key":"35_CR8","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1016\/j.apenergy.2017.11.098","volume":"211","author":"J Lago","year":"2018","unstructured":"Lago, J., Ridder, F.D., Vrancx, P., Schutter, B.D.: Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Appl. Energy 211, 890\u2013903 (2018)","journal-title":"Appl. Energy"},{"key":"35_CR9","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Graph convolutional recurrent neural network: Data-driven traffic forecasting. CoRR, abs\/1707.01926 (2017)"},{"key":"35_CR10","unstructured":"Lundberg, S., Lee, S.: A unified approach to interpreting model predictions. CoRR, abs\/1705.07874 (2017)"},{"key":"35_CR11","unstructured":"Mandi, J., Bucarey, V., Mulamba, M., Guns, T.: Predict and optimize: through the lens of learning to rank. CoRR, abs\/2112.03609 (2021)"},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/CJECE.2016.2586939","volume":"39","author":"H Mosbah","year":"2015","unstructured":"Mosbah, H., El-Hawary, M.: Hourly electricity price forecasting for the next month using multilayer neural network. Can. J. Electr. Comput. Eng. 39, 283\u2013291 (2015)","journal-title":"Can. J. Electr. Comput. Eng."},{"key":"35_CR13","unstructured":"PCR. Euphemia public description. Technical report, Price Coupling of Region (2016)"},{"key":"35_CR14","doi-asserted-by":"publisher","first-page":"118752","DOI":"10.1016\/j.apenergy.2022.118752","volume":"313","author":"L Tschora","year":"2022","unstructured":"Tschora, L., Pierre, E., Plantevit, M., Robardet, C.: Electricity price forecasting on the day-ahead market using machine learning. Appl. Energy 313, 118752 (2022)","journal-title":"Appl. Energy"},{"key":"35_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","volume":"43","author":"EI Vlahogianni","year":"2014","unstructured":"Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we\u2019re going. Transp. Res. Part C: Emerg. Technol. 43, 3\u201319 (2014)","journal-title":"Transp. Res. Part C: Emerg. Technol."},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Exploiting multi-channels deep CNNs for multivariate time series classification. University of China Hefei, Technical report (2015)","DOI":"10.1007\/s11704-015-4478-2"}],"container-title":["Lecture Notes in Computer Science","Advances in Intelligent Data Analysis XXI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30047-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:52:23Z","timestamp":1710359543000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30047-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031300462","9783031300479"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30047-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligent Data Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Louvain-la-Neuve","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ida2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ida2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}