{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:50:18Z","timestamp":1774453818703,"version":"3.50.1"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"vor","delay-in-days":105,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2019-ZD-0682"],"award-info":[{"award-number":["2019-ZD-0682"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007620","name":"Department of Education of Liaoning Province","doi-asserted-by":"publisher","award":["LNJC201912"],"award-info":[{"award-number":["LNJC201912"]}],"id":[{"id":"10.13039\/501100007620","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response (DR) not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short\u2010term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness and complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm (IGA) is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting.<\/jats:p>","DOI":"10.1155\/2021\/5571539","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T22:38:46Z","timestamp":1618612726000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Short\u2010Term Load Forecasting Model of LSTM Neural Network considering Demand Response"],"prefix":"10.1155","volume":"2021","author":[{"given":"Xifeng","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7273-9336","authenticated-orcid":false,"given":"Qiannan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoujin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"e_1_2_9_1_2","article-title":"SoC-based droop coefficients stability region analysis of the battery for stand-alone supply systems with constant power loads","volume":"99","author":"Wang R.","year":"2021","journal-title":"IEEE Transactions on Power Electronics"},{"key":"e_1_2_9_2_2","first-page":"1","article-title":"A hierarchical event detection method based on spectral theory of multidimensional matrix for power system","volume":"99","author":"Ma D.","year":"2019","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2412779"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.17775\/cseejpes.2017.01260"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/446\/2\/022034"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.matcom.2020.07.011"},{"key":"e_1_2_9_7_2","first-page":"4032","article-title":"Load forecasting based on multi-model by stacking ensemble learning","volume":"39","author":"Shi J.","year":"2019","journal-title":"Proceedings of the CSEE"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.2991316"},{"key":"e_1_2_9_9_2","article-title":"Efficient energy planning with decomposition-based evolutionary neural networks","volume":"99","author":"Ahmad T.","year":"2020","journal-title":"IEEE Access"},{"key":"e_1_2_9_10_2","first-page":"75","article-title":"A short-term load forecasting method based on EMD and characteristic correlation analysis","volume":"43","author":"Kong X.","year":"2019","journal-title":"Automation of Electric Power Systems"},{"key":"e_1_2_9_11_2","first-page":"77","article-title":"Application of interval time-series vector autoregressive model in short-term load forecasting","volume":"36","author":"Wan K.","year":"2012","journal-title":"Power System Technology"},{"key":"e_1_2_9_12_2","first-page":"134","article-title":"Short-term load forecasting based on a semi-parametric additive model","volume":"27","author":"Fan S.","year":"2010","journal-title":"Monash Econometrics & Business Stats Working Papers"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2013.2272326"},{"key":"e_1_2_9_14_2","first-page":"30","article-title":"Short-term spatial load forecasting based on partition and classification of power distribution information","volume":"31","author":"Wu Z.","year":"2019","journal-title":"Proceedings of the CSU-EPSA"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2975738"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2892475"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"SuoG. 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