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A Methodology to Identify Monthly Energy Use Models from Utility Bill Data for Seasonally Scheduled Buildings: Application to K-12 Schools
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The measured energy savings from retrofits in buildings is often determined as the difference between the energy consumption predicted by a baseline model and the measured energy consumption during the post retrofit period. Most baseline models are developed either by regressing the daily energy consumption versus the daily average temperature ( daily model ) or by regressing the monthly energy consumption versus the monthly average temperature ( monthly model ). Savings measurement for buildings such as primary and secondary schools (k-12 school) is very difficult due to the special operating schedules of such buildings. Currently, savings are either determined by simple pre-post utility bill comparison or by a method where by the baseline model consists of two separate models: a 3-P model for non-summer months, and a mean model for the summer months. (Landman, 1996). This paper proposes an improved methodology for identifying baseline models of energy use from utility billing data for buildings such as schools which have important daily and seasonal variations in occupancy. By explicitly considering the occupancy rate in the model, we are able to generalize it and retain the distinction between energy use levels during occupied and unoccupied days of the year. Thus the modified baseline model accounts, not only for the effect of weather, but also for the influence of school schedules. The proposed methodology has been evaluated against the previous 3-P-mean proposed by Landman for 10 schools in Texas for which several years of monitored data are available. Incorporation of scheduling information reduced the average CV of the model from 23.6% using Landman's method to 10.9% using our proposed method.
Wang, W.; Claridge, D. E.; Reddy, T. A. (1998). A Methodology to Identify Monthly Energy Use Models from Utility Bill Data for Seasonally Scheduled Buildings: Application to K-12 Schools. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from