{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T06:54:09Z","timestamp":1765608849725,"version":"3.37.3"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010880","name":"State Grid Corporation of China","doi-asserted-by":"publisher","award":["1300-202013387A-0-0-00"],"award-info":[{"award-number":["1300-202013387A-0-0-00"]}],"id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,11,29]]},"abstract":"Short-term load forecasting is an important part to support the planning and operation of power grid, but the current load forecasting methods have the problem of poor adaptive ability of model parameters, which are difficult to ensure the demand for efficient and accurate power grid load forecasting. To solve this problem, a short-term load forecasting method for smart grid is proposed based on multilayer network model. This method uses the integrated empirical mode decomposition (IEMD) method to realize the orderly and reliable load state data and provides high-quality data support for the prediction network model. The enhanced network inception module is used to adaptively adjust the parameters of the deep neural network (DNN) prediction model to improve the fitting and tracking ability of the prediction network. At the same time, the introduction of hybrid particle swarm optimization algorithm further enhances the dynamic optimization ability of deep reinforcement learning model parameters and can realize the accurate prediction of short-term load of smart grid. The simulation results show that the mean absolute percentage error \n \n \n \n e<\/a:mi>\n <\/a:mrow>\n \n MAPE<\/a:mtext>\n <\/a:mrow>\n <\/a:msub>\n <\/a:math>\n <\/jats:inline-formula> and root-mean-square error \n \n \n \n e<\/c:mi>\n <\/c:mrow>\n \n RMSE<\/c:mtext>\n <\/c:mrow>\n <\/c:msub>\n <\/c:math>\n <\/jats:inline-formula> of the performance indexes of the prediction model are 10.01% and 2.156\u2009MW, respectively, showing excellent curve fitting ability and load forecasting ability.<\/jats:p>","DOI":"10.1155\/2021\/8453896","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T23:05:09Z","timestamp":1638227109000},"page":"1-9","source":"Crossref","is-referenced-by-count":4,"title":["Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8226-7344","authenticated-orcid":true,"given":"Wei","family":"Guo","sequence":"first","affiliation":[{"name":"Market Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-5830","authenticated-orcid":true,"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1437-1569","authenticated-orcid":true,"given":"Xinjie","family":"Wei","sequence":"additional","affiliation":[{"name":"State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China"}]},{"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"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-017-0288-x"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.10.078"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4386"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2016.2594208"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.2991\/ijcis.d.200617.002"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.01.104"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2018.2807985"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3028649"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2018.2868167"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2018.6687"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1049\/joe.2018.8389"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3060654"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116991"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.4018\/ijamc.2021010108"},{"issue":"5","key":"15","doi-asserted-by":"crossref","first-page":"5397","DOI":"10.1109\/TSG.2018.2881562","article-title":"Robust regression models for load forecasting [J]","volume":"10","author":"J. 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Wang","year":"2018","journal-title":"Science Technology and Engineering"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-018-0380-x"},{"issue":"3","key":"24","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s12065-018-00196-0","article-title":"Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network","volume":"12","author":"K. 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