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• W1984603609 abstract "In this study we examine the use of airborne light detection and ranging (LiDAR) data to augment the prediction of building age and energy performance for numerous buildings using a random forests machine learning approach. Analysis is conducted for a residential neighborhood in Vancouver, Canada. Four separate models are developed to represent the increasing sophistication of spatial data available to municipalities. Results indicate that the random forests model using only LiDAR-derived building attributes predicts age with an average error (RMSE) of 16.8 years and 33.5% of the variance explained. When all attributes are combined from the various datasets the predictive capacity of the model is increased by 20% and the average error reduced to 15.8 years. Furthermore, examination of variable importance suggests that while mean building height derived from the LiDAR data is the most important attribute predicting age, the three subsequently ranked variables relate to non-LiDAR based attributes. Discussion is given to observable trends between select predictor variables and building age, and to the implications for building energy modeling and simulation efforts." @default.
• W1984603609 created "2016-06-24" @default.
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• W1984603609 date "2014-01-01" @default.
• W1984603609 modified "2023-10-02" @default.
• W1984603609 title "Predicting building ages from LiDAR data with random forests for building energy modeling" @default.
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• W1984603609 doi "https://doi.org/10.1016/j.enbuild.2013.10.004" @default.
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