Probabilistic optimal design concerning uncertainties and on-site adaptive commissioning of air-conditioning water pump systems in buildings

Hangxin Li and Shengwei Wang

Applied Energy, 2017, vol. 202, issue C, 53-65

Abstract: Sizing of air-conditioning water pump systems in buildings is a critical issue in design practice concerning the pump energy consumption in operation and risk of being undersized. As a result, significant energy is often wasted in operation due to oversizing to avoid the risk of being undersized. In current practice, throttling of commissioning valves are commonly adopted to push water flowrate (and pressure head) back to the design point no matter how much oversizing exists in a system. That partly mitigates the oversizing problem. This paper presents a novel approach consisting of probabilistic optimal design concerning uncertainties and on-site adaptive commissioning to further maximize energy savings of constant water flow pump systems. Minimized throttling is achieved by on-site adaptive commissioning, which reduces unnecessary pressure head and significant energy consumption. Pumps selected by the probabilistic optimal design can operate under both conventional design conditions and the projected possible off-design (oversized) conditions. The projection is based on the probability distribution of actual pressure head, which is estimated using Monte Carlo simulation by quantifying uncertainties in pressure loss calculation and system construction. Three case studies are conducted to test and validate this new design and commissioning approach. Results show that about 20% energy saving could be achieved, when the system is oversized by 20%, compared to conventional design and commissioning methods. The proposed approach also offers better energy performance in general compared to the designs all using variable speed pumps.

Keywords: Probabilistic optimal design; Uncertainty analysis; Adaptive commissioning; Air-conditioning; Building energy saving (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2017.05.131

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