{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:22:24Z","timestamp":1785410544743,"version":"3.56.0"},"reference-count":0,"publisher":"European Alliance for Innovation n.o.","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans Energy Web"],"abstract":"<jats:p>INTRODUCTION: With the advancement of global energy internet and smart grid, massive power big data with complex temporal features is generated by widely deployed smart meters. Accurate identification of power consumption behavior patterns is essential for dispatching a virtual power plant (VPP).OBJECTIVES: This paper proposes a fine-grained mining framework that combines temporal feature extraction and a graph neural network to support VPP dispatch requirements.METHODS: Linear interpolation and same-type day mean filling are used for data cleaning and normalization. A user relationship graph is constructed via physical connection and Pearson correlation-based behavioral similarity. A GCN-based unsupervised graph autoencoder is adopted for spatio-temporal feature embedding, and an improved DPC-FCM clustering algorithm is proposed to optimize it.RESULTS: The method effectively solves the inherent defects of traditional clustering: initial center sensitivity and noise interference.CONCLUSION: This framework realizes accurate fine-grained mining of power consumption patterns, providing reliable support for VPP dispatch decision-making.<\/jats:p>","DOI":"10.4108\/ew.12319","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:42:00Z","timestamp":1781617320000},"source":"Crossref","is-referenced-by-count":0,"title":["Mining method of power consumption behavior pattern of different subject resource objects in virtual power plant based on graph neural network"],"prefix":"10.4108","volume":"13","author":[{"given":"Duan","family":"Zhiguo","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02v4yxp84","id-type":"ROR","asserted-by":"publisher"}],"name":"State Grid Hebei Electric Power Company"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02v4yxp84","id-type":"ROR","asserted-by":"publisher"}],"name":"State Grid Hebei Electric Power Company"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02v4yxp84","id-type":"ROR","asserted-by":"publisher"}],"name":"State Grid Hebei Electric Power Company"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiakun","family":"An","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02v4yxp84","id-type":"ROR","asserted-by":"publisher"}],"name":"State Grid Hebei Electric Power Company"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyi","family":"Fan","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02v4yxp84","id-type":"ROR","asserted-by":"publisher"}],"name":"State Grid Hebei Electric Power Company"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2587","published-online":{"date-parts":[[2026,6,16]]},"container-title":["EAI Endorsed Transactions on Energy Web"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/12319\/4152","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/12319\/4152","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:42:01Z","timestamp":1781617321000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/view\/12319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":0,"URL":"https:\/\/doi.org\/10.4108\/ew.12319","relation":{},"ISSN":["2032-944X"],"issn-type":[{"value":"2032-944X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]}}}