AN INVERSE MODEL TO PREDICT AND EVALUATE THE ENERGYPERFORMANCE OF LARGE COMMERCIAL AND INSTITUTIONAL BUILDINGS

摘要

A new method for predicting and evaluating the energyrnperformance of large commercial and institutionalrnbuildings is developed, as an alternative to usingrnexisting comprehensive energy simulation programsrnlike DOE-2 and BLAST, or oversimplified tools likernanalyzing monthly utility bills. The steps arernconvenient and practical in their potential use by thernenergy analysts. The method mainly groups thernStepwise Multiple Linear Regression (SMLR) tornempirically model the dynamic building thermalrnperformance; Fourier Series to predict (extrapolate)rninternal loads; the Monte-Carlo Simulation to deal withrnthe prediction of internal loads probabilistically; and arnnew approach in normalizing weather conditions. Thernmethod is capable of predicting the building energyrnperformance in the pre-retrofit phase, identifiesrnappropriate energy conservation measures, andrnestimates potential energy savings.