Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings

Abstract

In recent years, many occupancy studies have used environmental sensor data (such as carbon dioxide [CO2], air temperature, and relative humidity) or Wi-Fi data to predict building occupancy information. However, the value of a data fusion approach that uses both environmental sensing and Wi-Fi sensing to predict occupancy remains an open question. To answer this question, this study conducted an on-site experiment in one office room in City University of Hong Kong. Three feature-based occupancy models using machine learning algorithms-k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN)-were selected to learn and predict occupancy information. In the model input, the study tested three data groups: environmental parameters only, Wi-Fi data only, and a combination of both. To assess the three occupancy models, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE) indices were utilized. Results showed that when only the environmental parameter data were applied to learn occupancy, the ANN-based occupancy model was more suitable and accurate, and so will be with the combination of environmental parameters and the Wi-Fi data. The SVM model is more suitable and accurate in learning occupancy information with Wi-Fi data. The ANN model is more suitable environment dataset and the combination. On the other hand, the combination of datasets cannot improve accuracy significantly during three days when compared with

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“…However, is the threshold of each node. Training of ANN is firstly started by forward propagation of the input data and hence the output of the hidden ( ) and output ( ) layers will be as follows [12,14].…”

Occupant behavior: a “new” factor in energy performance of buildings. Methods for its detection in houses and in offices.

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