{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:10:09Z","timestamp":1774541409797,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T00:00:00Z","timestamp":1479859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.<\/jats:p>","DOI":"10.3390\/a9040080","type":"journal-article","created":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T11:12:14Z","timestamp":1479899534000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuancheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"}]},{"given":"Panpan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-537X","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,23]]},"reference":[{"key":"ref_1","first-page":"3305","article-title":"Application technology of big data in smart distribution grid and its prospect analysis","volume":"12","author":"Zhao","year":"2014","journal-title":"Power Syst. 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