Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation

Ching-Wei Chen,Yung-Chung Chang

doi:10.1155/2016/6212951

Abstract

This study covers records of various parameters affecting the power consumption of air-conditioning systems. Using the Support Vector Machine (SVM), the chiller power consumption model, secondary chilled water pump power consumption model, air handling unit fan power consumption model, and air handling unit load model were established. In addition, it was found thatR2of the models all reached 0.998, and the training time was far shorter than that of the neural network. Through genetic programming, a combination of operating parameters with the least power consumption of air conditioning operation was searched. Moreover, the air handling unit load in line with the air conditioning cooling load was predicted. The experimental results show that for the combination of operating parameters with the least power consumption in line with the cooling load obtained through genetic algorithm search, the power consumption of the air conditioning systems under said combination of operating parameters was reduced by 22% compared to the fixed operating parameters, thus indicating significant energy efficiency.

Highlights

Conventional chillers require the highest power consumption among components of air conditioning systems
The air handling unit fan power consumption and the pump power consumption have a cubic relationship with air handling unit air flow and chilled water flow [23], which can be expressed as the following equations through multiple regression: Tan distribution optimization in order to obtain the least total power consumption
The outlet and inlet temperature of chilled water in the chiller, the outlet and inlet temperature of chilled water in the air handling unit, the outlet and inlet temperature of cooling water, chilled water flow, return air wet bulb temperature, chiller power consumption, pump power consumption, air handling unit power consumption, and other related data were compiled to calculate the actual load of the air handling unit

Summary

Introduction

Conventional chillers require the highest power consumption among components of air conditioning systems. Through the powerful search capacity of genetic algorithm [5,6,7], the optimized combination of outlet temperature of chilled water, the air flow of air handling unit, and secondary chilled water flow was searched simultaneously to obtain the least power consumption for cooling load. Schwedler and Bradley [8] proposed the correlations among chiller power consumption, air conditioning load, condenser, and outlet temperature of chilled water when the operating characteristics of each chiller vary. The air handling unit fan power consumption and the pump power consumption have a cubic relationship with air handling unit air flow and chilled water flow [23], which can be expressed as the following equations through multiple regression: Tan. distribution optimization in order to obtain the least total power consumption. Genetic algorithm was conjunctively used to search the optimized setting values of the three variables in order to obtain the least power consumption of the equipment under the various load conditions

System Structure

Genetic Algorithm

Results and Discussion

Conclusion