{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:17:21Z","timestamp":1762377441922,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2022A1515240074","2021A1515012245","2022A1515240074"],"award-info":[{"award-number":["2022A1515240074","2021A1515012245","2022A1515240074"]}]},{"name":"Guangdong Provincial Basic and Applied Basic Research Fund","award":["2022A1515240074","2021A1515012245","2022A1515240074"],"award-info":[{"award-number":["2022A1515240074","2021A1515012245","2022A1515240074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other load-influencing factors such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm (GA) to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system.<\/jats:p>","DOI":"10.3390\/e25091343","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T06:12:21Z","timestamp":1694931141000},"page":"1343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions"],"prefix":"10.3390","volume":"25","author":[{"given":"Min","family":"Xie","sequence":"first","affiliation":[{"name":"School of Electric Power, South China University of Technology, Guangzhou 510641, China"}]},{"given":"Shengzhen","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Electric Power, South China University of Technology, Guangzhou 510641, China"}]},{"given":"Kaiyuan","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Electric Power, South China University of Technology, Guangzhou 510641, China"}]},{"given":"Shiping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electric Power, South China University of Technology, Guangzhou 510641, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s43067-020-00021-8","article-title":"Electricity load forecasting: A systematic review","volume":"7","author":"Nti","year":"2020","journal-title":"J. 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