Visit the Energy Systems Laboratory Homepage.
Impact of Nighttime Shut Down on the Prediction Accuracy of Monthly Regression Models for Energy Consumption in Commercial Buildings
MetadataShow full item record
Regression models of measured energy use in buildings are widely used as baseline models to determine 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 impact of nighttime shut off on the accuracy 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 against the predictions of calibrated hourly 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 nighttime shut down, (iii) DDVAV system operating 24 hours per day, and (iv) DDVAV system with nighttime shut down. The results of the four cases studied indicate : 1) when the AHUs are operated 24 hours/day, the annual prediction error of the cooling regression models is less than 0.5% of the annual cooling energy consumption; however, 2) when the AHUs are operated with nighttime shut down, the annual prediction error of the cooling models becomes as high as 6% of annual energy consumption. It should be noted that the cases considered here include only single end-uses of energy and have not investigated energy-use data which includes multiple end-uses. Modified regression models are therefore recommended when AHUs are not operated 24 hours per day and the temperature pattern is significantly different between pre and post retrofit years.
Wang, J.; Claridge, D. E. (1998). Impact of Nighttime Shut Down on the Prediction Accuracy of Monthly Regression Models for Energy Consumption in Commercial Buildings. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from