{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:48:14Z","timestamp":1747190894037,"version":"3.40.5"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T00:00:00Z","timestamp":1647734400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Polytechnic","award":["NZ22003"],"award-info":[{"award-number":["NZ22003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2022,3,20]]},"abstract":"<jats:p>With the fast growing of new energy technologies, the proportion of distributed renewable energy sources dominated by wind and light energy in the microgrid continues to increase. However, the uncertainty and randomness of energy itself bring challenges to the stable operation of the power system. Microgrid load forecasting with high accuracy is the key means to handle the above problems. It can provide help for power grid dispatching and decision-making, optimize resource allocation, reduce operation cost, and ensure system safety. In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. Firstly, the criticality analysis of load influencing factors is carried out, and the clustering classification and weight calculation are completed. Then, the input data is preprocessed to ensure the quality of database. Secondly, the LSTM gets improved from three aspects: multilayer convolution channel, lookahead optimizer, and AM weight. And a complete forecasting model is designed to accomplish the load forecasting. Finally, based on the data of a local microgrid in Zhejiang Province, China, simulation experiments are conducted. The results are quantitatively compared with other forecasting algorithms to verify the accuracy and superiority of the proposed algorithm.<\/jats:p>","DOI":"10.1155\/2022\/4017708","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T03:13:59Z","timestamp":1647832439000},"page":"1-8","source":"Crossref","is-referenced-by-count":3,"title":["Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0375-1136","authenticated-orcid":true,"given":"Qiyue","family":"Huang","sequence":"first","affiliation":[{"name":"Ningbo Polytechnic, Ningbo 315800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Zheng","sequence":"additional","affiliation":[{"name":"Ningbo Center for CEEC Expo & Cooperation Promotion, Ningbo 315800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Ningbo Polytechnic, Ningbo 315800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.02.015"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2013.2258691"},{"key":"3","unstructured":"ZhangC.Research on some issues of short-term wind speed forecasting for wind farms2017Ph.D. dissertation"},{"issue":"13","key":"4","first-page":"74","article-title":"Combined prediction model of wind farm output power","volume":"33","author":"L. Chun","year":"2009","journal-title":"Power System Technology"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2018.2858265"},{"issue":"5","key":"6","first-page":"133","article-title":"Short-term load forecasting based on deep belief network[J]","volume":"42","author":"K. O. N. G. Xiangyu","year":"2018","journal-title":"Automation of Electric Power Systems"},{"issue":"8","key":"7","first-page":"131","article-title":"Short-term load forecasting method based on CNN-LSTM hybrid neural network mode","volume":"43","author":"L. U. Jixiang","year":"2019","journal-title":"Automation of Electric Power Systems"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.02.069"},{"key":"9","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.enconman.2019.05.007","article-title":"One dimensional convolutional neural network architectures for wind prediction","volume":"195","author":"H. Shubhi","year":"2019","journal-title":"Energy Conversion and Management"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/tste.2012.2232944"},{"key":"11","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.energy.2019.03.081","article-title":"Short-term forecasting by using a combined method of convolutional neural networks and fuzzy time series","volume":"175","author":"J. S. Hossein","year":"2019","journal-title":"Energy"},{"key":"12","article-title":"Large scale spatial electric load forecasting framework based on spatial convolution[J]","volume":"117","author":"D. A. G. Vieira","year":"2019","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106151"},{"key":"14","first-page":"376","article-title":"Prediction of wind farm power ramp rates: a data-mining approach","volume":"3","author":"Z. Haiyang","year":"2009","journal-title":"Journal of Solar Energy Engineering"},{"author":"H. Zareipour","key":"15","article-title":"Wind power ramp events classification and forecasting\uff1aa data mining approach"},{"issue":"1","key":"16","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/TSTE.2015.2477244","article-title":"An optimized swinging door algorithm for identifying wind ramping events","volume":"7","author":"M. Cui","year":"2015","journal-title":"IEEE Transactions on Sustainable Energy"},{"issue":"12","key":"17","first-page":"6","article-title":"Prediction of wind power ramp based on sparse decomposition of atoms and BP neural network","volume":"38","author":"C. Mingjian","year":"2014","journal-title":"Automation of Electric Power Systems"},{"issue":"2","key":"18","first-page":"572","article-title":"Wind power ramp events forecast method based on similarity correction","volume":"37","author":"T. Ouyang","year":"2017","journal-title":"Proceeding of the CSEE"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2018.5781"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/4017708.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/4017708.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/4017708.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T03:14:16Z","timestamp":1647832456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2022\/4017708\/"}},"subtitle":[],"editor":[{"given":"Fran\u00e7ois","family":"Vall\u00e9e","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,3,20]]},"references-count":19,"alternative-id":["4017708","4017708"],"URL":"https:\/\/doi.org\/10.1155\/2022\/4017708","relation":{},"ISSN":["2090-0155","2090-0147"],"issn-type":[{"type":"electronic","value":"2090-0155"},{"type":"print","value":"2090-0147"}],"subject":[],"published":{"date-parts":[[2022,3,20]]}}}