Annual prediction accuracy of monthly regression models for energy consumption in commercial buildings - prelminary results

摘要

Regression models of measured energy use in commercial buildings are widely used as baseline models for determining retrofit savings from measured energy consumption.It is less expensive to determine savings from monthly utility bills when they are available than to install hourly metering equipment However,little is known about the acuracy of savings determined from monthly data.This paper reports a preliminary investigation of this question by comparing the heating and cooling energy use predicted by regression models based on monthly data with the predictions of calibrated horurly simulation models when applied to a medium-sized university building in Texas with (i) DDCAV system operating 24 hours per day, (ii) DDCAV system with unoccupied shut down, (iii) DDVAV system operating 24 hours per day, and (iv) DDVAV system with unoccuped shut down.