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A Dynamic Controller for Residential Energy Management at the Intersection of Occupant Thermal Comfort and Dynamic Electricity Price

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Centrally controlled heating, ventilation, and air conditioning (HVAC) systems in commercial buildings are operated by building management systems (BMS) based on the predefined operational settings and a set of assumptions. Despite the high rate of energy consumption by HVAC systems in commercial buildings, observations showed that a significant portion of the occupants remain dissatisfied with thermal conditions. One of the main reasons is that HVAC systems do not take into account personalized comfort preferences in their operational rules. This study proposes a framework to integrate building occupants in the HVAC control loop, learn their comfort profiles, and control the HVAC system based on occupants' personalized comfort profiles. The framework fuses occupants' comfort perception indices (i.e., comfort votes provided by users and mapped to a numerical value), collected through participatory sensing, and ambient temperature data, collected through a sensor network, and computes occupants' comfort profiles by using a fuzzy rule-based descriptive and predictive model. The performance of the comfort-profiling algorithm was assessed using human subject data and synthetically generated data. For actuation, a BMS controller was proposed and tested in two zones of an office building. The BMS controller uses a proportional controller algorithm that regulates room temperatures to be equidistant from preferred temperatures of all occupants in the same thermal zone. Validation of the framework components demonstrated that the nonlinear underlying pattern of the thermal comfort sensation scale could accurately be recognized. Results of the BMS controller experiments revealed that the proportional controller algorithm is capable of keeping the thermal zones' temperatures in the ranges of preferred temperatures.
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A personalized measure for thermal comfort has been applied for use in combination with smart controls for building automation. Using data from a field study, we first show the superiority of personalized measures for thermal comfort compared to standard non-adaptive methods. Based on this knowledge we describe a methodology, using logistic regression techniques, to convert user votes to a probability of comfort. We also describe the interface used to collect the votes. We show that, for a given subject, our thermal profile converges against the probabilities found in the field study. As a case study we implemented the measure in a control algorithm to control the shading devices. The results clarify the mode of action and also show the effectiveness of the method.
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