{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T20:32:50Z","timestamp":1759177970235,"version":"3.40.5"},"reference-count":28,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,10,12]]},"abstract":"<jats:p>For the problems of low accuracy and low efficiency of most load forecasting methods, a load forecasting method based on improved deep learning in cloud computing environment is proposed. Firstly, the preprocessed data set is divided into several data partitions with relatively balanced data volume through spatial grid, so as to better detect abnormal data. Then, the density peak clustering algorithm based on spark is used to detect abnormal data in each partition, and the local clusters and abnormal points are merged. The parallel processing of data is realized by using spark cluster computing platform. Finally, the deep belief network is used for load classification, and the classification results are input into the empirical mode decomposition-gating recurrent unit network model, and the load prediction results are obtained through learning. Based on the load data of a power grid, the experimental results demonstrate that the mean prediction error of the proposed method is basically controlled within 3% in the short term and 0.023\u2009MW, 19.75%, and 2.76% in the long term, which are better than other comparison methods, and the parallel performance is good, which has a certain feasibility.<\/jats:p>","DOI":"10.1155\/2021\/3250732","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T05:13:36Z","timestamp":1634102016000},"page":"1-11","source":"Crossref","is-referenced-by-count":5,"title":["Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-5830","authenticated-orcid":true,"given":"Kai","family":"Zhang","sequence":"first","affiliation":[{"name":"State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8226-7344","authenticated-orcid":true,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Market Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3309-2928","authenticated-orcid":true,"given":"Jian","family":"Feng","sequence":"additional","affiliation":[{"name":"State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5268-9060","authenticated-orcid":true,"given":"Mei","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Tsingsoft Technology Co., Ltd., Beijing 100085, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.04.115"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1142\/s0218126620500103"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.5207\/jieie.2020.34.11.037"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.17816\/transsyst20206180-91"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2019.08.011"},{"issue":"1","key":"6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.12677\/SA.2020.91009","article-title":"Short-term load forecasting based on variable weighted synthesis of different kernel SVM","volume":"9","author":"D. 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