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Comparison of the Prediction Accuracy of Daily and Monthly Regression Models for Energy Consumption in Commercial Buildings
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The measured energy savings from retrofits in commercial buildings are generally determined as the difference between the energy consumption predicted using a baseline model and the measured energy consumption during the post retrofit period. Most baseline models are developed by regressing the daily energy consumption versus the daily average temperature (daily models) or by regressing the monthly energy consumption versus the monthly average temperature (monthly models). Since the post-retrofit weather is generally different from the weather used for model development, the prediction error of the baseline model may be different from the fitting error. Daily and monthly baseline models were developed for a midsize commercial building with (i) dual-duct CAV and VAV systems, (ii) office and university occupancy schedules, and (iii) different operating practices using the weather of a mild weather year. The prediction errors were identified as the difference between the energy use predicted by the regression models and the values simulated by a calibrated simulation program when both models use weather from a year very different from the weather year used to develop the regression model. The major results are summarized below: 1. When the AHUs operate 24 hours per day, annual energy prediction errors of daily regression models were found to be less than 1.4%. The errors of monthly regression models were found to be in the same range as the error of the daily models. 2. When the AHUs were shut down during unoccupied periods, annual prediction errors for both daily and monthly regression models were as high as 15%. However, the prediction error of daily regression models can be decreased to a range of 2% to 3% if the daily average energy consumption is regressed versus the average temperature during the operation period. Based on these findings, we suggest use of daily or monthly regression models when the AHUs are operated 24 hours per day. When shut-down is performed during unoccupied hours, daily energy consumption should be regressed versus the average ambient temperature during operating hours to develop the baseline model.
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Wang, Jinrong (1996). Comparison of the Prediction Accuracy of Daily and Monthly Regression Models for Energy Consumption in Commercial Buildings. Master's thesis, Texas A&M University. Available electronically from
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