{"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":"
\n

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>\n","protected":false},"excerpt":{"rendered":"

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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","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-post-option":[],"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 2012","msr_affiliation":"","msr_published_date":"2012-11-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"205766","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"EarlyOff%20-%20BuildSys%202012.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/EarlyOff20-20BuildSys202012.pdf","id":205766,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":205766,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/EarlyOff20-20BuildSys202012.pdf"}],"msr-author-ordering":[{"type":"text","value":"Carl 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 Krumm"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144633,144767],"msr_project":[170664],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170664,"post_title":"PreHeat: Controlling Home Heating With Occupancy Prediction","post_name":"preheat-controlling-home-heating-with-occupancy-prediction","post_type":"msr-project","post_date":"2011-03-02 12:43:04","post_modified":"2023-11-28 10:06:40","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/preheat-controlling-home-heating-with-occupancy-prediction\/","post_excerpt":"Home heating uses more energy than any other residential energy expenditure, making increasing the efficiency of home heating an important goal for saving money and protecting the environment. We have built a home-heating system, PreHeat, that automatically programs your thermostat based on when you are home. PreHeat\u2019s goal is to reduce the amount of time a household\u2019s thermostat needs to be on without compromising the comfort of household members. 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