{"id":161547,"date":"2011-09-01T00:00:00","date_gmt":"2011-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/preheat-controlling-home-heating-using-occupancy-prediction\/"},"modified":"2021-10-15T02:05:16","modified_gmt":"2021-10-15T09:05:16","slug":"preheat-controlling-home-heating-using-occupancy-prediction","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/preheat-controlling-home-heating-using-occupancy-prediction\/","title":{"rendered":"PreHeat: Controlling Home Heating Using Occupancy Prediction"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Home heating is a major factor in worldwide energy use. Our system, PreHeat, aims to more efficiently heat homes by using occupancy sensing and occupancy prediction to automatically control home heating. We deployed PreHeat in five homes, three in the US and two in the UK. In UK homes, we controlled heating on a per-room basis to enable further energy savings. We compared PreHeat\u2019s prediction algorithm with a static program over an average 61 days per house, alternating days between these conditions, and measuring actual gas consumption and occupancy. In UK homes PreHeat both saved gas and reduced MissTime (the time that the house was occupied but not warm). In US homes, PreHeat decreased MissTime by a factor of 6-12, while consuming a similar amount of gas. In summary, PreHeat enables more efficient heating while removing the need for users to program thermostat schedules.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Home heating is a major factor in worldwide energy use. Our system, PreHeat, aims to more efficiently heat homes by using occupancy sensing and occupancy prediction to automatically control home heating. We deployed PreHeat in five homes, three in the US and two in the UK. In UK homes, we controlled heating on a per-room [&hellip;]<\/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":[246574],"research-area":[13556,13552,13554],"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-161547","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-highlight-award","msr-research-area-artificial-intelligence","msr-research-area-hardware-devices","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"","msr_affiliation":"","msr_published_date":"2011-9-1","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":"Ten year lasting impact award","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":"206399","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/ubic282-scott.pdf","id":"206399","title":"ubic282-scott.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":206399,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/ubic282-scott.pdf"}],"msr-author-ordering":[{"type":"user_nicename","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":"user_nicename","value":"A.J. 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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. PreHeat System PreHeat builds a predictive&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170664"}]}},{"ID":170560,"post_title":".NET Gadgeteer","post_name":"net-gadgeteer","post_type":"msr-project","post_date":"2010-09-20 09:28:47","post_modified":"2023-05-26 12:07:34","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/net-gadgeteer\/","post_excerpt":"Microsoft .NET Gadgeteer is a rapid prototyping platform for small electronic gadgets and embedded hardware devices. It combines the advantages of object-oriented programming, solderless assembly of electronics using a kit of hardware modules, and quick physical enclosure fabrication using computer-aided design. Microsoft .NET Gadgeteer is a rapid prototyping platform for small electronic gadgets and embedded hardware devices. 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