{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:23:43Z","timestamp":1730247823941,"version":"3.28.0"},"reference-count":13,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,23]]},"DOI":"10.1109\/iciscae55891.2022.9927364","type":"proceedings-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T21:32:10Z","timestamp":1667597530000},"page":"16-21","source":"Crossref","is-referenced-by-count":0,"title":["Short-term photovoltaic power generation forecasting method based on optimized long and short memory neural networks"],"prefix":"10.1109","author":[{"given":"Hao","family":"Li","sequence":"first","affiliation":[{"name":"State Grid Anhui Electric Power Company Limited Lu'an Power Supply Company,Lu'an,China"}]},{"given":"Junjie","family":"Ma","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Co., Ltd,Hefei,China"}]},{"given":"Wuyan","family":"Hu","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Company Limited Lu'an Power Supply Company,Lu'an,China"}]},{"given":"Wenhe","family":"Zhuo","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Co., Ltd,Hefei,China"}]},{"given":"Zhou","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Anhui Electric Power Co., Ltd,Hefei,China"}]}],"member":"263","reference":[{"key":"ref10","first-page":"742","article-title":"Shortterm prediction of coal stock in power plant based on singular spectrum analysis and long short-term memory neural network [J]","volume":"49","author":"wen","year":"2020","journal-title":"Information and Control"},{"key":"ref11","first-page":"161","article-title":"Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction [J]","author":"d\u00e1vid","year":"2022","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"ref12","first-page":"309","article-title":"Caputo derivative applied to very short time photovoltaic power forecasting [J]","author":"davide","year":"2022","journal-title":"Applied Energy"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.egyr.2021.09.167","article-title":"Photovoltaic power prediction of LSTM model based on Pearson feature selection [J]","volume":"7","author":"hailang","year":"2021","journal-title":"Energy Reports"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2016.04.089"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2018.08.005"},{"key":"ref6","article-title":"ARMA model of the solar power station based on output prediction [J]","volume":"2","author":"lan","year":"2011","journal-title":"Electrical Measurement & Instrumentation"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2015.02.100"},{"key":"ref8","first-page":"67","article-title":"Short-term PV power prediction based on particle swarm optimization combined with chaos crossover for deep belief networks [J]","volume":"57","author":"hui","year":"2020","journal-title":"Electrical Measurement & Instrumentation"},{"key":"ref7","first-page":"640","article-title":"Research on model temperature prediction method based on fireworks algorithm and long and short time memory network [J]","volume":"41","author":"dianfan","year":"2020","journal-title":"ACTA Metrologica Sinica"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2015.06.017"},{"key":"ref1","article-title":"Deep Learning based forecasting of photovoltaic power generation via theory-guided LSTM","author":"luo","year":"2020","journal-title":"EnerarXiv Preprint"},{"key":"ref9","first-page":"64","article-title":"Forecasting of photovoltaic power generation probability density based on similar day and quantile regression forests [J]","volume":"48","author":"feng","year":"2019","journal-title":"Thermal Power Generation"}],"event":{"name":"2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE)","start":{"date-parts":[[2022,9,23]]},"location":"Dalian, China","end":{"date-parts":[[2022,9,25]]}},"container-title":["2022 IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9927385\/9927503\/09927364.pdf?arnumber=9927364","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T20:22:27Z","timestamp":1669666947000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9927364\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":13,"URL":"https:\/\/doi.org\/10.1109\/iciscae55891.2022.9927364","relation":{},"subject":[],"published":{"date-parts":[[2022,9,23]]}}}