| The optimal control and operation of HVAC system with real-time occupancy schedules can effectively reduce the building energy consumption.In existing studies,carbon dioxide concentration and WiFi signals are direct and indirect representation of occupant information(e.g.presence or count)which are widely used for occupancy estimation.However,due to its inherent characteristics,they have certain advantages and limitations.Carbon dioxide concentration shows relatively better performance in inferring high level of occupancy while its performance is strongly affected by delay issue.On the contrary,Wi-Fi sensing technologies can easily and timely obtain Wi-Fi signals which is suitable for real-time estimation.The limitation of using Wi-Fi signal is that they are easily influenced by signal loss or noise signals.A suitable combination of using Wi-Fi signals and carbon dioxide concentration can overcome their limitations.Accordingly,the main objective of this study is to develop an effective method to reasonably combine the carbon dioxide and Wi-Fi signals so as to improve the accuracy of occupancy estimation.Specifically,a new pre-processing method based on door opening status was proposed in this study to address the issue of Wi-Fi signals.With the preprocessed data,a dynamic coupling prediction model that optimizes the use of carbon dioxide concentration and Wi-Fi signals was established.In order to demonstrate the effectiveness of the proposed method,35-days on-site measurement of occupancy behavior in an office building was carried out.The model performance was evaluated and verified for weekday and weekend based on the measured data.And the model performance is also compared with single parameter model(i.e.the model only use carbon dioxide concentration or Wi-Fi signals).In addition,to further analyze the performance of the proposed model,the predictive accuracy and respective influencing factors were analyzed for weekday and weekend,respectively.The main conclusions are summarized as follows:(1)The new pre-processing method for Wi-Fi data can distinguish indoor signals from outdoor signals effectively and recognize the Wi-Fi signals that belongs to occupants in a given room,which provides a basis for developing the occupancy estimation models.(2)When the increment of carbon dioxide concentration is above 500 ppm and below 250 ppm,the carbon dioxide concentration and the number of Wi-Fi signals should be used as a stand-alone parameter to infer occupancy pattern,respectively.When the increment of carbon dioxide concentration ranges between 250 ppm and 500 ppm,a weighted linear combination of carbon dioxide concentration and the number of Wi-Fi signals is used to predict the occupancy level.Results indicated that the prediction accuracy of the dynamic coupling model is about 10% higher than that of the single parameter model.(3)Due to the significantly different occupancy patterns in weekday and weekend,the weight value of the weighted linear combination of the two parameters needs to be determined by the degree of signal loss in Wi-Fi data and the delay in carbon dioxide concentration in the building.In terms of the model influencing factors,the time step of transition probability has less influence on the prediction accuracy while the selection of simulation time step significantly affect the prediction accuracy.Results showed that the prediction accuracy is the highest when the simulation step size and the model transition probability were both set as 10 minutes.Future studies need to focus on the identification of appropriate increment value of carbon dioxide concentration which is crucial for the success of model application.Thus more experiments would be carried out for different room types.Also,the integration of the proposed model with different HVAC system operation strategies will be studied in the future so as to evaluate corresponding energy saving potentials in practical application. |
