The ventilation load providing outdoor air intake is one of the dominant heating/cooling loads in buildings, and increased ventilation rates play an important role in decreasing the percentage of subjects dissatisfied with IAQ and contribute to an improvement in workplace productivity. In order to optimize the ventilation rate, the application of an energy recovery ventilator (ERV) integrated CO_2 demand controlled algorithm will be an effective measure in indoor environmental design. The overarching objective of this study is to develop an optimized ERV with a CO_2 demand controlled algorithm. Here, the residual lifetime of CO_2 generated at a local point in an office space was measured to discuss and improve the feedback algorithm of CO_2 sensing at a local point and the adjustment of the ventilation rate. In this paper (Part 2 of this research project), we conducted long-term field measurements in a university office space that installed ERVs with CO_2-demand-control systems and reported the results under winter condition. In this study, two types of CO_2DCV algorithms, i.e., multistage CO_2 concentration threshold setting methods that adjust the ventilation rate in a stepwise manner, and consecutive/sequential control of the ventilation rate according to time-dependent CO_2 concentrations, were adopted. During the field measurements in 2013FY, a sequential optimization procedure of the ventilation rate using an inverter system was introduced into the ERV with CO_2DCV, and a significant reduction inoutdoor intake and electricity consumption was confirmed.
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