Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.apenergy.2021.118343
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dc.titleUsing Google Trends as a proxy for occupant behavior to predict building
dc.contributor.authorFu, Chun
dc.contributor.authorMiller, Clayton
dc.date.accessioned2022-07-29T05:18:56Z
dc.date.available2022-07-29T05:18:56Z
dc.date.issued2022-03-15
dc.identifier.citationFu, Chun, Miller, Clayton (2022-03-15). Using Google Trends as a proxy for occupant behavior to predict building. APPLIED ENERGY 310 : 10.1016/j.apenergy.2021.118343. ScholarBank@NUS Repository. https://doi.org/10.1016/j.apenergy.2021.118343
dc.identifier.issn03062619
dc.identifier.issn18729118
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/229410
dc.description.abstractIn recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each building. This study proposes an approach that utilizes the search volume of topics (e.g., education or Microsoft Excel) on the Google Trends platform as a proxy of occupant behavior and use of buildings. Linear correlations were first examined to explore the relationship between energy meter data and Google Trends search terms to infer building occupancy. Prediction errors before and after the inclusion of the trends of these terms were compared and analyzed based on the ASHRAE Great Energy Predictor III (GEPIII) competition dataset. The results show that highly correlated Google Trends data can effectively reduce the overall RMSLE error for a subset of the buildings to the level of the GEPIII competition's top five winning teams’ performance. In particular, the RMSLE error reduction during public holidays and days with site-specific schedules are respectively reduced by 20–30% and 2–5%. These results show the potential of using Google Trends to improve energy prediction for a portion of the building stock by automatically identifying site-specific and holiday schedules.
dc.language.isoen
dc.publisherELSEVIER SCI LTD
dc.sourceElements
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectEnergy & Fuels
dc.subjectEngineering, Chemical
dc.subjectEngineering
dc.subjectGoogle Trends
dc.subjectMachine learning
dc.subjectKaggle competition
dc.subjectModel error reduction
dc.subjectBuilding energy prediction
dc.subjectEnergy model
dc.subjectENERGY-CONSUMPTION
dc.subjectCOOLING LOAD
dc.subjectSIMULATION
dc.subjectMACHINE
dc.typeArticle
dc.date.updated2022-07-19T00:41:36Z
dc.contributor.departmentTHE BUILT ENVIRONMENT
dc.description.doi10.1016/j.apenergy.2021.118343
dc.description.sourcetitleAPPLIED ENERGY
dc.description.volume310
dc.description.page10.1016/j.apenergy.2021.118343
dc.published.statePublished
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