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In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai\u2019an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9\u2009:\u20091 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.<\/jats:p>","DOI":"10.1155\/2021\/1502932","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T22:35:22Z","timestamp":1635287722000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The Short\u2010Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai\u2032an, Shandong Province, China"],"prefix":"10.1155","volume":"2021","author":[{"given":"Jiuyun","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3747-8369","authenticated-orcid":false,"given":"Huanhe","family":"Dong","sequence":"additional","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\/0000-0003-0861-5870","authenticated-orcid":false,"given":"Yuan","family":"Kong","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2005.860944"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/su13041694"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.06.012"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.027"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.99410"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/2475-8876.12135"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"LiW.andZhangZ. based on time sequence of Arima model in the application of short-term electricity load forecasting Proceedings of the 2009 International Conference on Research Challenges in Computer Science December 2009 Shanghai China 11\u201314 https:\/\/doi.org\/10.1109\/ICRCCS.2009.12 2-s2.0-77949920672.","DOI":"10.1109\/ICRCCS.2009.12"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.221222"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"JuberiasG. YuntaR. MorenoJ. G. JuberiasG. andMendivilR. C. A new ARIMA model for hourly load forecasting 1 Proceedings of the 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333). IEEE April 1999 New Orleans LA USA 314\u2013319 https:\/\/doi.org\/10.1109\/TDC.1999.755371.","DOI":"10.1109\/TDC.1999.755371"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.932287"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpas.1971.293123"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"JiP. XiongD. WangP. andChenJ. 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