Analysis of parameter adjustment and efficiency improvement of optimized energy-saving retrofit of building clusters using the least-squares approach

The energy-saving performance of large building clusters has gradually become a focus of attention in the construction of green and low-carbonized cities. In this paper, we select K university building cluster as a research sample, collect its building outline, city information point (POI) and area boundary information, and generate its building cluster energy consumption model through spatial analysis and building type identification. For the prediction of energy consumption and energy-saving renovation of the building cluster, the multi-criteria decision optimization theory is introduced to predict the energy consumption of the energy-saving scheme, and the least squares method is used to establish the functional relationship between building energy consumption and outdoor climate, and to comprehensively build the energy consumption prediction and optimization model of the building cluster. The model can fully take into account the energy-saving technologies applied to many different types of buildings in K university, and the difference between the predicted and actual energy-saving performance data is controlled within 3.00%, which shows high feasibility in promoting the optimization and enhancement of energy-saving effect of buildings.