{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T02:52:51Z","timestamp":1781837571070,"version":"3.54.5"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","funder":[{"name":"Liaoning Province Applied Basic Research Program Project of China","award":["2023JH2\/101300065"],"award-info":[{"award-number":["2023JH2\/101300065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,9,15]]},"abstract":"<jats:p> One of the biggest difficulties, which China\u2019s power infrastructure is now experiencing, is the capacity to estimate demand of short-term electricity. It can accurately estimate changes in the total power load in particular locations. Accurate forecasting results, which also serve as a dependable guide for power system operation, may increase the flexibility and resource usage of the contemporary power market. Power load characteristics are impacted by several things. This study presents an estimate method for short-term demand of electricity based on WOA-BILSTM in order to comprehensively evaluate the time series features presented in the power load data and improve the accuracy of power load forecasting. After extracting features for the variables that influence power load, this method uses the bidirectional long-term and short-term memory (BILSTM) neural network layer for bidirectional time series feature learning. Using the local electricity load data from Quanzhou from 2018 as the data set, the prediction model is constructed by screening the multi-dimensional input parameters and selecting the feature vectors with good association as the input carefully. By comparing the outcomes of three popular load forecasting models, including LSTM network, BILSTM network, and WOA-BILSTM, it is demonstrated that the WOA-BILSTM neural network method is more accurate and effective than others two. By building an optimal combination model, this technique could increase the accuracy of power load data\u2019s short-term predictions. At the same time, it also reduces the time of personnel debugging. <\/jats:p>","DOI":"10.1142\/s0218001423590188","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T16:18:05Z","timestamp":1690561085000},"source":"Crossref","is-referenced-by-count":12,"title":["Forecasting of the Short-Term Electricity Load Based on WOA-BILSTM"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9598-4279","authenticated-orcid":false,"given":"Huaxin","family":"Zhao","sequence":"first","affiliation":[{"name":"Electrical Engineering College of Shenyang Institute of Engineering, Shenyang, P. R. China"},{"name":"Shenyang Key Laboratory of Energy Internet, Intelligent Perception and Security Technology, Shenyang, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8226-1859","authenticated-orcid":false,"given":"Zhenliu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shenyang Key Laboratory of Energy Internet, Intelligent Perception and Security Technology, Shenyang, P. R. China"},{"name":"Information College of Shenyang Institute of Engineeringm, Shenyang, P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9507-7130","authenticated-orcid":false,"given":"Pizhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenyang Key Laboratory of Energy Internet, Intelligent Perception and Security Technology, Shenyang, P. R. China"},{"name":"Information College of Shenyang Institute of Engineeringm, Shenyang, P. R. 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