Estimating building thermal properties by integrating heat transfer inversion model with clustering and regression techniques for a portfolio of existing buildings

Abstract

A static heat transfer model is derived from a system of dynamic equations by integrating the dynamic equations over different time periods. That static heat transfer model links periodic (e.g., monthly) energy usage with cooling and heating degree hours, humidifying and dehumidifying hours. Its coefficients of measuring correlations correspond to the thermal parameters of buildings. Temporal data from a building may be used to estimate the overall heat transfer parameters. A clustering scheme may be developed to decompose all the buildings into different clusters based on one or more similarity criteria. The overall heat transfer parameters are separated into values for the wall, roof and window using multiple buildings' data in the same cluster or group.