Given the importance of recognizing indoor occupant's location and activity intensity to the control of intelligent homes, exploring approaches that can detect occupants' location and activity types while protecting occupants' privacy is helpful. In this study, occupants' zonal location and activity intensity recognition models were developed using passive infrared (PIR) sensors and machine learning algorithms. A PIR sensor array with 15 nodes was employed to monitor indoor occupant's and cat's behavior in a case residential building for 71 days. The output signals of PIR sensors varied with different locations and activity intensities. By analyzing the PIR data feature, models were established using six machine learning algorithms and two sets of data. After comparing model performance, the support vector machine (SVM) algorithm was selected to establish the final models. The model input was optimized by accumulating the PIR data. Taking PIR original counting values in 1-minute and 30-minute accumulated data as input features, the optimized SVM model can achieve 99.7% accuracy under the 10-fold cross-validation for the training data set, and 90.9% accuracy for the test data set. The cat's activity intensity is much weaker than that of occupant, yielding much smaller PIR output, which helped the SVM model to distinguish cat's activity from occupant's with > 90% accuracy. The model's recognition accuracy decreases with the decrease of sensor numbers and nine sensors were necessary. The findings obtained in this study support the promising future of applying PIR sensors in smart homes.

The personal comfort system (PCS) aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption. To date, many PCS devices have been developed and evaluated from the perspective of thermal comfort. It will be useful for future PCS development if an approach to quantify the thermal comfort and energy performance of certain PCS devices and their combinations with consideration of user behaviors can be established. This study attempted to fill this gap by integrating thermal comfort experiments, occupancy simulations, usage behavior modeling, and building energy simulation technologies. First, human subject experiments were conducted to quantify the thermal comfort effects of the PCS. Then, the Markov chain model and conditional probability model were employed to describe the room occupancy and PCS usage behaviors. Finally, the extended comfort temperature range and user behavior models were imported into the building energy simulation tool to analyze the energy-saving potential of the PCS. The results show that the use of PCS can significantly improve occupants' thermal comfort and satisfaction rate under both warm and cool conditions. Using a cooling cushion and desktop fan can lift the upper limit of the comfortable temperature to 29.5 ℃ while the heated cushion can extend the lower limit to 15 ℃. By increasing the air conditioning temperature setpoint by 2 ℃ in summer and reducing by 2.5 ℃ the heating temperature setpoint in winter, PCS devices can reduce heating and air conditioning energy consumption by 25%–40% while maintaining occupants' thermal comfort.

Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability. The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use. Most of these applications, however, use only one type of variable importance approaches. Therefore, this paper proposes a procedure of conducting two types of variable importance analysis (predictive and variance-based) to determine robust and effective energy saving measures in urban buildings. These two variable importance methods belong to metamodeling techniques, which can significantly reduce computational cost of building energy simulation models for urban buildings. The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs, while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition. The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy, cooling energy, electricity, and carbon emissions of buildings. The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings. The carbon emissions can be reduced approximately 30% after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60% of reduction of carbon emissions. Moreover, this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.