{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:36:57Z","timestamp":1760240217461,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T00:00:00Z","timestamp":1554854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JST CREST","award":["JPMJCR1304"],"award-info":[{"award-number":["JPMJCR1304"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression model (local expert) for each partition on the heterogeneous mixture modeling, and an extended goal graph that extracts candidates of variables both for data partitioning and for linear regression for the energy prediction algorithm. These methods were implemented as tools and applied to create the energy prediction model on two years' hourly consumption data for a building. We validated the methods by comparing accuracies with those of different machine learning algorithms applied to the same datasets.<\/jats:p>","DOI":"10.3390\/info10040134","type":"journal-article","created":{"date-parts":[[2019,4,10]],"date-time":"2019-04-10T11:25:08Z","timestamp":1554895508000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling"],"prefix":"10.3390","volume":"10","author":[{"given":"Noriyuki","family":"Kushiro","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ami","family":"Fukuda","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masatada","family":"Kawatsu","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshihiro","family":"Mega","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,10]]},"reference":[{"key":"ref_1","unstructured":"Agency for Natural Resources and Energy in Japan (2019, April 08). 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Proceedings of the 20th International Conference on Knowledge Based and Intelligent Information and Engineering System, KES2016, York, UK."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ifuku, M., Kushiro, N., and Aoyama, Y. (2018, January 17\u201320). Requirements definition with extended goal graph. Proceedings of the IEEE International Conference on Data Mining, Singapore.","DOI":"10.1109\/ICDMW.2018.00039"},{"key":"ref_13","unstructured":"OpenARD Alliance (2019, April 08). OpenADR2.0 Specifications. Available online: https:\/\/www.openadr.org."},{"key":"ref_14","unstructured":"ASHRAE (2019, April 08). A Data Communication Protocol for Building Automation and Control Networks. Available online: http:\/\/www.bacnet.org\/Overview\/index.html."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mega, T., Kitagami, S., Kawawaki, S., and Kushiro, N. (2017, January 27\u201329). 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R for Everyone: Advanced Analytics and Graphics, Addison-Wesley."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/4\/134\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:44:20Z","timestamp":1760186660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/4\/134"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,10]]},"references-count":24,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["info10040134"],"URL":"https:\/\/doi.org\/10.3390\/info10040134","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2019,4,10]]}}}