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However, most of the existing load forecasting models lack more detailed considerations for some special days. In this paper, the applicability of the bagged regression trees (BRT) model combined with eight variables is investigated to forecast short\u2010term load in Qingdao. The comparative experiments show that the accuracy and speed of forecasting have some improvements using the BRT than the artificial neural network (ANN). Then, an indicator variable is newly proposed to capture the abnormal information during special days, which include national statutory holidays, bridging days, and proximity days. The BRT model combined with this indicator variable is tested on the load series measured in 2018. Experiments demonstrate that the improved model generates more accurate predictive results than BRT model combined with previously variables on special days.<\/jats:p>","DOI":"10.1155\/2021\/3693294","type":"journal-article","created":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T20:05:15Z","timestamp":1631822715000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["The Short\u2010Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3747-8369","authenticated-orcid":false,"given":"Huanhe","family":"Dong","sequence":"first","affiliation":[]},{"given":"Ya","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5515-3758","authenticated-orcid":false,"given":"Yong","family":"Fang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2951-3603","authenticated-orcid":false,"given":"Mingshuo","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0861-5870","authenticated-orcid":false,"given":"Yuan","family":"Kong","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2013.2252929"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2966712"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.496169"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-017-0288-x"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.99410"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAS.1971.293123"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2011.2161780"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2012.06.009"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2016.10.067"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","unstructured":"SinghS. 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