{"id":163586,"date":"2012-11-01T00:00:00","date_gmt":"2012-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/earlyoff-using-house-cooling-rates-to-save-energy\/"},"modified":"2018-10-16T19:56:07","modified_gmt":"2018-10-17T02:56:07","slug":"earlyoff-using-house-cooling-rates-to-save-energy","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/earlyoff-using-house-cooling-rates-to-save-energy\/","title":{"rendered":"EarlyOff: Using House Cooling Rates To Save Energy"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Home heating systems often have a significant thermal inertia, as homes stay warm after the heating is turned off for significant periods of time. We present the EarlyOff concept, whereby home heating can be predictively turned off in advance of occupants\u2019 departure, using this inertia to keep the house warm while saving energy. We use a previously gathered data set of real-time heating, gas, and occupancy readings from five houses and conduct a data-driven analysis of potential energy savings. Using an \u201coracle\u201d predicting actual departure events, we show an upper bound savings of 4\u201312% of the gas used over the whole study period by applying EarlyOff. Using a real predictor which makes use of historical occupancy probabilities, we show savings of 1\u20138% of gas use.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Home heating systems often have a significant thermal inertia, as homes stay warm after the heating is turned off for significant periods of time. We present the EarlyOff concept, whereby home heating can be predictively turned off in advance of occupants\u2019 departure, using this inertia to keep the house warm while saving energy. We use [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13552,13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-163586","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-hardware-devices","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"Proceedings of BuildSys 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Ellis","user_id":0,"rest_url":false},{"type":"edited_text","value":"James Scott","user_id":32459,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=James Scott"},{"type":"text","value":"Mike Hazas","user_id":0,"rest_url":false},{"type":"edited_text","value":"John Krumm","user_id":32203,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=John 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