Heat Load Modelling for Air Conditioned Room Using Buckingham-Pi Theorem

Abstract

Because the electricity peak demand and the electricity consumption in Thailand have increased every year. Moreover, the energy statistic shows that the energy consumption for air-conditioning system is accounted for about 60% of the total electricity consumption of a residential section in Thailand. Also, the ministry of energy of Thailand reports that statistical data of electricity consumption demand for air-conditioning has increased by 350 MW for every 1°C temperature increased of thermostat set-point. With this issue, the increasing of thermostat set-point becomes an important key to reduce energy used in air-conditioning systems. Normally, the human thermal comfort is used to define the thermostat set-point of air-conditioners. When the thermal comfort zone for occupant in air-conditioned rooms can be increased, the electricity peak demand will decrease. In this study, the thermal comfort was investigated with respect to ASHRAE standard. In addition, most of air-conditioners manufacturing is designed the commercial air-conditioners as a fixed size, while the clients are usually chosen air-conditioners oversize to the heat load of air-conditioned rooms. This occurrence leads to an overconsumption of energy use in air-conditioners. Hence, an investigation for determining the proper heat load of air-conditioned rooms was considered as another essential key in order to reduce energy consumption of air-conditioners. The research methodology was conducted using a survey on the thermal comfort of humans that rested in air-conditioned room. The results of the survey were then used as the criteria of thermostat setting. Moreover, the mathematical relation of air-conditioner’s power with respect to the heat load of air-conditioned room was investigated. Based on the thermal comfort condition, this was used to define heat load modelling condition, as well as to investigate the preliminary application for estimating the reduction potential of electricity demand peak and electricity consumption of air-conditioners. The thermal comfort survey was performed in the air-conditioned room and the space temperature was controlled by two air-conditioners. Moreover, the humidity was controlled by a humidifier that could add water vapour with a maximum relative humidity of the room of at least 80%. For this thermal comfort survey, the bachelor’s degree students of Chiang Mai University with the number’s sample size of at least 395 students was used in order to achieve the confidence level of 95%. At the beginning, the students were asked to provide information for investigating the results of human comfort. After that, the heat load investigation was started to prepare an experimental room, as well as to find the geometry and engineering properties of the experimental room’s components. Then, the air-conditioners and the measurement device were installed and collected data of at least 1-minute interval time throughout the tests with respect to the conditions of the within internal heat load generation and the without internal heat load generation. At the end, the results of heat load investigation were used to generate the heat load model by using the Buckingham Pi technique to simplify the heat load equation and then to verify heat load model with different times of collecting data. The results of the thermal comfort survey showed that a range of operative temperature was 23.6 to 27.4°C at 90% occupant’s acceptability; moreover, most occupants were preferred a relative humidity range of 40-50%. Besides, the results of heat load of airconditioned room showed that the comparison of heat load consumption and cooling capacity consumption had an average error of about 6.8%. In this study, the Buckingham- Pi technique was used to generate heat load models and simplified heat load equation with less functional variables. Although the varied Π groups of heat load model were reported, the rate of heat load model of the fourteenth Π group was chosen due to an error of 6.56% as compared to the rate of heat load experiment. The results also showed that the number of samples gives a significant effect on the error of predicted model, as a smaller number of samples was resulted in a larger error of prediction. Furthermore, the rate of heat load model verifying was able to use for predicting the heat load, as the same trend of both rates was clearly presented. In this study, the error between the rate of heat load model verifying and the rate of heat load experiment verifying was about 11.5%, which could be explained by the range of ambient air temperature for verifying process that was wider than for the modelling process. Additionally, the results of a reduction potential of electricity peak demand and an electricity consumption by air-conditioners showed that at the upper limit of thermal comfort (27°C) gave a significant reduction in electricity peak than at the lower limit of thermal comfort (25°C). For the condition of without internal heat load generation, the electricity consumption obtained from the condition of thermostat setting of 25°C was 23.7% higher than the condition of thermostat setting of 27°C. Meanwhile, for the condition of with internal heat load generation the electricity consumption obtained from the condition of thermostat setting of 25°C was 30.7% higher than the condition of thermostat setting of 27°C.