{"type":"ScholarlyArticle","title":"Forecasting Multi-Appliance Usage for Smart Home Energy Management","authors":[{"name":"Ngoc Cuong Truong","slug":"ngoc-cuong-truong","given":"Ngoc Cuong","family":"Truong","affiliation":""},{"name":"James McInerney","slug":"james-mcinerney","given":"James","family":"McInerney","affiliation":""},{"name":"Long Tran-Thanh","slug":"long-tran-thanh","given":"Long","family":"Tran-Thanh","affiliation":""},{"name":"Enrico Costanza","slug":"enrico-costanza","given":"Enrico","family":"Costanza","affiliation":""},{"name":"Sarvapali D. Ramchurn","slug":"sarvapali-d-ramchurn","given":"Sarvapali D.","family":"Ramchurn","affiliation":""}],"abstract":"We address the problem of forecasting the usage of multiple electrical appliances by domestic users, with the aim of providing suggestions about the best time to run appliances in order to reduce carbon emissions and save money (assuming time-of-use pricing), while minimising the impact on the users' daily habits.An important challenge related to this problem is the modelling the everyday routine of the consumers and of the inter-dependencies between the use of different appliances. Given this, we develop an important building block of future home energy management systems: a prediction algorithm, based on a graphical model, that captures the everyday habits and the inter-dependency between appliances by exploiting their periodic features.We demonstrate through extensive empirical evaluations on real-world data from a prominent database that our approach outperforms existing methods by up to 47%.","doi":null,"url":"https://ijcai.org/proceedings/2013/427","pdf":"https://ijcai.org/proceedings/2013/0427.pdf","markdown":"https://ijcai.org/proceedings/2013/427.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/427","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":427,"pages":{"first":2908,"last":2913},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}