Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings

Heating, Ventilation, and Air-Conditioning (HVAC) control strategies are set arbitrarily in many commercial buildings by operators, who sometimes lack relevant skills and professional training. It is acknowledged that improving the control strategy of HVAC is feasible and valid, which as a consequence can improve the overall HVAC performance of existing buildings. However, it is quite difficult for an outsiders or a commissioning agent to tell what the HVAC control strategies are and whether they are implemented appropriately in existing buildings. This paper is intended to carry out analysis on the data about Building Automation System (BAS), as well as the data about building energy, for the purpose of identifying the control strategies of HVAC in a given building by using data mining algorithm. Then the results can be adopted by us to determine whether the building is under faulty operation or is running under suboptimal conditions. In this paper, what are proposed are algorithms of data mining identification for some specific HVAC control strategies, including DR on/off strategy, DR reset strategy and temperature reset strategy of chilled water. On the basis of data mining algorithms, a framework is then developed so as to identify these strategies, and the main scenario of this identification framework is known as analyzing many commercial buildings on an energy monitoring platform of a public building. This framework takes the sensor data obtained from HVAC, including temperature, flowrate, and electricity usage, as input, which is followed by the application of Image Segmentation and PCA algorithm for preprocessing. Then, based on these input variables, XGBoost algorithm is employed to determine whether these strategies have been implemented in buildings or not. In order to get the data for training and testing the framework, EnergyPlus Runtime Language is adopted for the application of different strategies. t is finally shown by the result that the identification algorithm can achieve the accuracy rate of 92.5% in the case studies by using one-day operation data, and the identification algorithm can arrive at the accuracy rate of 100% by using three-day operation data.

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