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dc.contributor.authorPan, Yueen_US
dc.contributor.authorZhang, Limaoen_US
dc.identifier.citationPan, Y. & Zhang, L. (2020). Data-driven estimation of building energy consumption with multi-source heterogeneous data. Applied Energy, 268, 114965-.
dc.description.abstractFor better energy evaluation and management, a categorical boosting (CatBoost)-based predictive method is presented to accurately estimate building energy consumption by learning large volumes of multi-source heterogeneous data collected from buildings. To be specific, the newly-developed CatBoost model belonging to the ensemble learning has superiority in handling categorical variables and producing reliable results. As a case study, our proposed method is validated in a multi-dimensional dataset about Seattle's building energy performance provided by the city's government, aiming to estimate the weather normalized site energy use intensity of buildings and characterize its non-linear relationship with other 12 possible influential features. Results from the 5-fold cross-validation demonstrate that the model exhibits a strong ability in predicting the exact value of energy intensity precisely, which can even outperform popular machine learning algorithms including random forest and gradient boosting decision tree under R2 of 0.897. Based on a defined threshold, these predicted values can be classified as the normal or abnormal energy consumption reaching an accuracy of 99.32% for outlier detection, which is helpful in alarming potential risks at an early stage and developing strategies to enhance the energy efficiency. Moreover, results from the established model can be interpreted objectively, suggesting that features concerning the physical and energy characteristics contribute more to energy estimation than environmental features. Since such results understand the building energy consumption and efficiency in a data-driven manner, they can eventually serve as guidance for building owners and designers in designing and renovating buildings to achieve better energy-conserving performance.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.relation.ispartofApplied Energyen_US
dc.rights© 2020 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleData-driven estimation of building energy consumption with multi-source heterogeneous dataen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.subject.keywordsData Miningen_US
dc.subject.keywordsFeature Importanceen_US
dc.description.acknowledgementThe Ministry of Education Tier 1 Grant, Singapore (No. M4011971.030) and the Start-Up Grant at Nanyang Technological University, Singapore (No. M4082160.030) are acknowledged for their financial support of this research.en_US
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