{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T20:16:21Z","timestamp":1730232981107,"version":"3.28.0"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"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":[[2021,10,14]]},"DOI":"10.1109\/iccais52680.2021.9624627","type":"proceedings-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T16:21:26Z","timestamp":1639066886000},"page":"110-115","source":"Crossref","is-referenced-by-count":0,"title":["Ultra-Short-Term Multistep Building Power Prediction Based on WD-BO-LSTM"],"prefix":"10.1109","author":[{"given":"Yu","family":"Binbin","sequence":"first","affiliation":[]},{"given":"Li","family":"Jianjing","sequence":"additional","affiliation":[]},{"given":"Sun","family":"Bo","sequence":"additional","affiliation":[]},{"given":"Liu","family":"Shuai","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Residential Load Short-term Forecasting Based on Improved BP Neural Network[J]","volume":"37","year":"2018","journal-title":"Foreign Electronic Measurement Technology"},{"key":"ref11","article-title":"Short-term Load Forecasting Method Based on CNN-LSTM Hybrid Neural Network Model[J]","volume":"43","year":"2019","journal-title":"Automation of Electric Power Systems"},{"key":"ref12","article-title":"Ultra-short-term Power Load Forecasting Based on LSTM and XGBOOST Combined Model[J]","volume":"44","year":"2020","journal-title":"Power System Technology"},{"key":"ref13","article-title":"Short-term power load forecasting based on Elman neural network[J]","volume":"39","year":"2018","journal-title":"Modern Business Trade Industry"},{"journal-title":"Electrical Engineering Materials","article-title":"Short-term load forecasting model based on lifting artificial neural network[J]","year":"2021","key":"ref14"},{"key":"ref15","article-title":"Short-term Power Load Prediction Based on Dropout-ILSTM Network[J]","volume":"58","year":"2021","journal-title":"Electrical measurement and Instrumentation"},{"key":"ref16","article-title":"Short-term load prediction of distribution transformer with electric heating based on EEMD-BP neural network[J]","volume":"55","year":"2018","journal-title":"Electrical measurement and Instrumentation"},{"key":"ref17","article-title":"Load forecasting of distribution network based on improved K-means and DE-ELM[J]","volume":"38","year":"2019","journal-title":"Foreign Electronic Measurement Technology"},{"key":"ref18","article-title":"Shortterm load forecasting of buildings based on meteorological and movement data[J]","volume":"44","year":"2019","journal-title":"Journal of Geomatics"},{"key":"ref19","article-title":"Research on fine short-term load forecasting model considering meteorological factors[J]","volume":"34","year":"2019","journal-title":"Journal of Electric Power"},{"key":"ref4","article-title":"A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features[J]","volume":"14","year":"2021","journal-title":"Energies"},{"key":"ref3","first-page":"1","article-title":"A hybrid robust system considering outliers for electric load series forecasting[J]","year":"2021","journal-title":"Applied Intelligence"},{"key":"ref6","first-page":"1","article-title":"Sliding window-based LightGBM model for electric load forecasting using anomaly repair[J]","year":"2021","journal-title":"The Journal of Supercomputing"},{"journal-title":"Mathematical Problems in Engineering","article-title":"A Power Load Forecasting Model Based on FA-CSSA-ELM[J]","year":"2021","key":"ref5"},{"key":"ref8","first-page":"1883","article-title":"Application of Variational Mode Decomposition and Deep Learning in Short-Term Power Load Forecasting[J]","year":"0","journal-title":"Journal of Physics Conference Series"},{"key":"ref7","first-page":"1852","article-title":"Power Load Forecasting Model Based on Deep Neural Network[J]","year":"0","journal-title":"Journal of Physics Conference Series"},{"key":"ref2","first-page":"7","article-title":"Short-term Power Load Forecasting based on Machine Learning[J]","year":"2021","journal-title":"International Core Journal of Engineering"},{"key":"ref1","first-page":"1871","article-title":"The forecast of household power load based on genetic algorithm optimizing BP neural network[J]","year":"0","journal-title":"Journal of Physics Conference Series"},{"key":"ref9","article-title":"User Load Interval Prediction Method Based on LSTM[J]","volume":"31","year":"2018","journal-title":"Industrial Control Computer"},{"key":"ref20","article-title":"Short-term load forecasting method based on EEMD-EN-SVR[J]","volume":"41","year":"2020","journal-title":"Journal of Hubei Normal University (Natural Science)"},{"key":"ref22","first-page":"187","article-title":"Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM[J]","year":"2019","journal-title":"Energy"},{"key":"ref21","article-title":"Building electrical load forecasting method based on time-series multi-scale decomposition[J]","volume":"42","year":"2020","journal-title":"Journal of Southeast University (Natural Science Edition)"}],"event":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","start":{"date-parts":[[2021,10,14]]},"location":"Xi'an, China","end":{"date-parts":[[2021,10,17]]}},"container-title":["2021 International Conference on Control, Automation and Information Sciences (ICCAIS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9624464\/9624202\/09624627.pdf?arnumber=9624627","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:53:43Z","timestamp":1652187223000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9624627\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/iccais52680.2021.9624627","relation":{},"subject":[],"published":{"date-parts":[[2021,10,14]]}}}