Development of an inverse method to estimate overall building and ventilation parameters of large commercial buildings

We propose an inverse method to estimate building and ventilation parameter s from non-intrusive monitoring of heating and cooling thermal energy use o f large commercial buildings. The procedure involves first deducing the loa ds of an ideal one-zone building from the monitored data, and then in the f ramework of a mechanistic macromodel, using a multistep linear regression a pproach to determine the regression coefficients (along with their standard errors) which can be finally translated into estimates of the physical par ameters (along with the associated errors). Several different identificatio n schemes have been evaluated using heating and cooling data generated from a detailed building simulation program for two different building geometri es and building mass at two different climatic locations. A multistep ident ification scheme has been found to yield very accurate results, and an expl anation as to why it should be so is also given. This approach has been sho wn to remove much of the bias introduced in multiple linear regression appr oach with correlated regressor variables. We have found that the parameter identification process is very accurate when daily data over an entire pear are used. Parameter identification accuracy using twelve monthly data poin ts and daily data over three months of the year was also investigated. Iden tification with twelve monthly data paints seems to be fairly accurate whil e that using daily data over a season does not yield very good results. Thi s latter issue needs to be investigated further because of its practical re levance.