{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:33Z","timestamp":1750220013537,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":9,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,3,17]]},"DOI":"10.1145\/3594315.3594391","type":"proceedings-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:14:16Z","timestamp":1691021656000},"page":"685-690","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Power Load Forecasting Method Based on Big Data And Machine Learning Hybrid Network Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6794-0255","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"the PLA Strategic Support Force Information Engineering University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6101-8751","authenticated-orcid":false,"given":"Yiqun","family":"Zhou","sequence":"additional","affiliation":[{"name":"the PLA Strategic Support Force Information Engineering University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8824-549X","authenticated-orcid":false,"given":"Kaixiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"the PLA Strategic Support Force Information Engineering University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7431-9139","authenticated-orcid":false,"given":"Xuemei","family":"Hou","sequence":"additional","affiliation":[{"name":"the PLA Strategic Support Force Information Engineering University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Short-term electricity load forecasting based on ARIMA model[J]","author":"Chenxi Li","year":"2015","unstructured":"Li Chenxi . \u201c Short-term electricity load forecasting based on ARIMA model[J] ,\u201d Zhuhai Power Supply Bureau of Guangdong Power Grid Co, Ltd , 2015 .06.007. Li Chenxi. \u201cShort-term electricity load forecasting based on ARIMA model[J],\u201d Zhuhai Power Supply Bureau of Guangdong Power Grid Co, Ltd, 2015.06.007."},{"key":"e_1_3_2_1_2_1","first-page":"147","article-title":"A random forest and short and long term memory network based approach to electricity load forecasting[J]","volume":"2022","author":"Yanjun Dong","unstructured":"Dong Yanjun , Wang Xiaotian , Ma Hongming , Wang Libin , Li Mengyu , Yue Fanding , Yuan Huan . \u201c A random forest and short and long term memory network based approach to electricity load forecasting[J] ,\u201d Global Energy Internet , 2022 ,5(02): 147 - 156 . Dong Yanjun, Wang Xiaotian, Ma Hongming, Wang Libin, Li Mengyu, Yue Fanding, Yuan Huan. \u201cA random forest and short and long term memory network based approach to electricity load forecasting[J],\u201d Global Energy Internet,2022,5(02):147-156.","journal-title":"Global Energy Internet"},{"key":"e_1_3_2_1_3_1","volume-title":"Research on short-term load forecasting based on deep learning [D]","author":"Sitong Liu","year":"2021","unstructured":"Liu Sitong . \u201c Research on short-term load forecasting based on deep learning [D] ,\u201d Shenyang University of Technology , 2021 . Liu Sitong. \u201cResearch on short-term load forecasting based on deep learning [D],\u201d Shenyang University of Technology, 2021."},{"key":"e_1_3_2_1_4_1","volume-title":"Short-term load forecasting based on dynamic similar day selection with improved Stacking integration learning[D]","author":"Yanqiang Duan","year":"2021","unstructured":"Duan Yanqiang . \u201c Short-term load forecasting based on dynamic similar day selection with improved Stacking integration learning[D] ,\u201d Liaoning University of Engineering and Technology , 2021 . Duan Yanqiang. \u201cShort-term load forecasting based on dynamic similar day selection with improved Stacking integration learning[D],\u201d Liaoning University of Engineering and Technology, 2021."},{"key":"e_1_3_2_1_5_1","volume-title":"Research on load forecasting method based on Stacking integrated learning [D]","author":"Zhenqi Tan","year":"2021","unstructured":"Tan Zhenqi . \u201c Research on load forecasting method based on Stacking integrated learning [D] ,\u201d Guizhou University , 2021 . Tan Zhenqi. \u201cResearch on load forecasting method based on Stacking integrated learning [D],\u201dGuizhou University,2021."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.12720\/jait.13.1.29-35"},{"issue":"5","key":"e_1_3_2_1_7_1","first-page":"450","article-title":"APredictive Model for Depression Risk in Thai Youth during COVID-19","volume":"13","author":"Nuankaew Wongpanya S.","year":"2022","unstructured":"Wongpanya S. Nuankaew , Patchara Nasa-ngium, Prem Enkvetchakul , and Pratya Nuankaew , \u201c APredictive Model for Depression Risk in Thai Youth during COVID-19 ,\u201d Journal of AdvancesinInformation Technology , Vol. 13 , No. 5 , pp. 450 - 455 , October 2022 . Wongpanya S. Nuankaew, Patchara Nasa-ngium, Prem Enkvetchakul, and Pratya Nuankaew, \u201cAPredictive Model for Depression Risk in Thai Youth during COVID-19,\u201d Journal of AdvancesinInformation Technology, Vol. 13, No. 5, pp. 450-455, October 2022.","journal-title":"Journal of AdvancesinInformation Technology"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.12720\/jait.11.2.91-96"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.12720\/jait.12.1.14-20"}],"event":{"name":"ICCAI 2023: 2023 9th International Conference on Computing and Artificial Intelligence","acronym":"ICCAI 2023","location":"Tianjin China"},"container-title":["Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3594315.3594391","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3594315.3594391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:16Z","timestamp":1750182676000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3594315.3594391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":9,"alternative-id":["10.1145\/3594315.3594391","10.1145\/3594315"],"URL":"https:\/\/doi.org\/10.1145\/3594315.3594391","relation":{},"subject":[],"published":{"date-parts":[[2023,3,17]]},"assertion":[{"value":"2023-08-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}