• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.2
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• pp.86-96
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• 2010

The paper describes development and evaluation of a simple method for determining gradient of modified linear setpoint variation to reduce peak electrical cooling demand in buildings using building precooling and setpoint adjustment. The method is an approximated approach for minimizing electrical cooling demand during occupied period in buildings and involves modified linear adjustment of cooling setpoint temperature between $26^{\circ}C$ and $28^{\circ}C$. The gradient of linear variation or final time of linear increase is determined based on the cooling load shape in conventional cooling control having a constant setpoint temperature. The potential to reduce peak cooling demand using the simple method was evaluated through building simulation for a calibrated office building model considering four different weather conditions. The simple method showed about 30% and 20% in terms of reducing peak cooling demand and chiller power consumption, respectively, compared to the conventional control.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.10
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• pp.467-473
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• 2014

Since the operating conditions of HVAC systems are different from those for which they are designed, on-going commissioning is required to optimize the energy consumed and the environment in the building. This study presents a methodology to analyze operational data and its applications. A predicted operation model is to be produced through a statistical data analysis using multiple regressions in SPSS. In this model, the dependent variable is the pre-cooling time, and the independent variables include the power output of the supply air inverter during pre-cooling, the supply air set temperature during pre-cooling, the indoor temperature-indoor set temperature just before pre-cooling, supply heat capacity, and the lowest outdoor air temperature during non-cooling/non-heating hours. The correlation coefficient R2 of the multiple regression model between the pre-cooling hour and the internal/external factors is of 0.612, and this could be used to provide information related to energy conservation and operating guidance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4610-4615
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• 2013

In this study, optimal operation strategies to save the electric energy and power price in the building central cooling system is researched by experiments. The optimal strategies of demand response control and outdoor temperature reset control algorithms are applied by consideration the electric energy and power price according to the energy consumption characteristics. The suggested optimal control method shows better responses in the power price and energy consumption in comparison with the conventional one and saves energy consumption by 9.5% and electronic price by 15.7%, respectively.