Paper Conference

Proceedings of ASim Conference 2016: 3rd Asia conference of IBPSA-China, Japan, Korea


Determination of the Optimum Set-point to Balance the Thermal Comfort and Operation Energy Consumption

S. Cho, H. Dong, J. Jeong

Abstract: The objective of this research is to suggest a method that can be used to obtain the optimal control set-point that considers weights of thermal comfort and heating, ventilation, and air conditioning (HVAC) energy consumption; this is done using a multi objective genetic algorithm (MOGA). An artificial neural network (ANN) is adopted in order to determine a prediction model for the thermal load, HVAC energy consumption and thermal comfort based on the properties of individual buildings. In this research, 64 building cases were simulated with Energy Plus and Open Studio to produce synthetic data. To make a thermal load model, 14 parameters that are representative of building thermal load were selected. In order to obtain the HVAC energy consumption model, 5 parameters that have a strong influence on the HVAC energy consumption were selected. After the prediction models were built, the MOGA tool in MATLAB was used for optimization. The simulation results of optimization showed that the control variables set by MOGA, which improved the thermal comfort, led to only a small increase in the HVAC energy consumption compared to the base case when weight factors were biased against thermal comfort.
Keywords: Optimal set-point, Prediction model, Genetic algorithm, Artificial neural network, HVAC energy