Energy saving effect prediction and post evaluation of air-conditioning system in public buildings

Abstract

Accurate energy saving effect evaluation analysis of building energy efficiency retrofit is of benefit to obtain technology optimization and fast return of investment. According to the implement sequence, evaluation methods can be divided into post evaluation and prediction evaluation. The energy saving effect of an air-conditioning system retrofit project was analyzed by these two models respectively. The post evaluation model was built based on the spot test data and a parameter called as Refrigeration Operation Energy saving Effect Ratio (ROEER). The prediction evaluation model was built based on Back-Propagation Artificial Neural Network by the use of MATLAB Neural Network Toolbox. The comparison result between these two kinds of evaluation models match well with each other. These two models can be used to predict and evaluate energy saving effect of air-conditioning system retrofit to further improve the real energy saving effect of building energy efficiency retrofit.