02356nas a2200229   4500008004100000022001300041245012200054210006900176260001200245300001400257490000700271520146000278653001601738653002601754653002101780653002501801653001801826100001401844700001601858700001901874856023301893       2018                        eng d  a0926580500aOccupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings0 aOccupancy prediction through machine learning and data fusion of  c10/2018  a233 - 2430 v943 a<p>Occupancy information is crucial to building facility design, operation, and energy efficiency. Many studies propose the use of environmental sensors (such as carbon dioxide, air temperature, and relative humidity sensors) and radio-frequency sensors (Wi-Fi networks) to monitor, assess, and predict occupancy information for buildings. As many methods have been developed and a variety of sensory data sources are available, establishing a proper selection of model and data source is critical to the successful implementation of occupancy prediction systems. This study compared three popular machine learning algorithms, including k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN), combined with three data sources, including environmental data, Wi-Fi data, and fused data, to optimize the occupancy models' performance in various scenarios. Three error measurement metrics, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE), have been employed to compare the models' accuracies. Examined with an on-site experiment, the results suggest that the ANN-based model with fused data has the best performance, while the SVM model is more suitable with Wi-Fi data. The results also indicate that, comparing with independent data sources, the fused data set does not necessarily improve model accuracy but shows a better robustness for occupancy prediction.</p>10adata fusion10aenvironmental sensing10aMachine learning10aoccupancy prediction10aWi-Fi sensing1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen  uhttps://linkinghub.elsevier.com/retrieve/pii/S0926580518302656https://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/plain