| Summary: | Compressor-based cooling contributes a large part of energy consumption in buildings, especially in hot and humid climates. In Singapore, electricity comprises the largest percentage of building operating expense, of which about 50% is for air conditioning. Increasing cooling setpoint and utilizing elevated air speed generated by fans is a viable and promising approach of delivering thermal comfort with less energy use. The work presents in this thesis leverages on recent advances in human thermal comfort research and indoor positioning techniques to develop cost-effective, energy-efficient and occupant-satisfied solutions for indoor location-based cooling. Specifically, the occupant-aware operations of both a single fan and a system of fans are introduced. In addition, an indoor tracking system with a more generalized noise model for obtaining more accurate occupancy information is developed. The effectiveness of the proposed solutions is demonstrated through simulations and experiments. For personalized thermal comfort, a smart tracking fan system is developed using camera-based indoor localization. The position of the occupant is constantly tracked by a camera-based localization system and subsequently used for determining the occupant-fan distance and direction of airflow. Personal cooling is provided upon the detection of the occupant in the area bounded by virtual geofences. The state-of-art PMV-SET (Predicted Mean Vote-Standard Effective Temperature) thermal comfort model is used to obtain the relationship between the air speed and thermal comfort. This relationship is used to determine the desired air speed under different environmental conditions. The fan is then controlled to give the desired air speed, taking into account the occupant-fan distance. For multiple occupants, a patented technology for cooperatively controlling a system of fans to provide optimized air movement is developed. A cost-effective calibration method to obtain the relationship between air speed and fan speed setting is proposed to predict airflow in the actual environment. Fans operation is optimized by minimizing the maximum deviation between the actual air speed generated by fans and the desired air speed. This minimax-error problem is reformulated as a linear programming problem which can be solved readily using standard methods. To obtain occupancy information, a received signal strength (RSS)-based indoor tracking system is implemented. A common problem with RSS-based indoor tracking is the disturbances encountered in dynamic and complex indoor environments. The proposed system uses the generalized t-distribution (GT) to model the disturbances. A recursive filtering algorithm based on the GT noise model is developed. Because of the more accurate noise model, the proposed filter can produce better position estimates than that of the Kalman filter which makes the usual assumption of Gaussian noise. An equation to compute the variance of the estimation error is also derived. The variance equation can be used as an analytic tool for designing and assessing the tracking system. Both theoretical and experimental results show that the variance of the estimation error from the proposed filter is less than that from the Kalman filter. The experiments also show that the filter with GT noise model can handle outlier better than the Kalman filter.
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