A fusion framework for occupancy estimation in office buildings based on environmental sensor data

Occupancy information that can be used to determine heating, ventilation and lighting requirements is one of the important parameters for the control of energy efficient buildings. In this paper, we propose a fusion framework for building occupancy estimation with environmental parameters. Based on the environmental sensor data, a coarse estimation of building occupancy can be achieved using data-driven models that include extreme learning machine (ELM), support vector machine (SVM), artificial neural network (ANN), K-nearest neighbors (KNN), linear discriminant analysis (LDA) and classification and regression tree (CART). Due to the extremely fast learning speed of the ELM algorithm, we apply an ELM-based wrapper method to select the best feature set of environmental parameters. To further improve the estimation accuracy of building occupancy, taking occupancy dynamics into consideration, we fuse the results of data-driven models with well developed occupancy models by using a particle filter algorithm. Real experiments have shown that our proposed fusion framework can achieve improvements of around 5-14% and 3-12% for the estimation accuracy and the detection accuracy (presence/absence) respectively among the different methodologies.