Modelling Industrial End Use Electricity Consumption using Statistically Adjusted Engineering Estimates

Kenneth H. Tiedemann


Statistically adjusted engineering estimates , Identification, Modelling, Energy efficiency


To better understand how large industrial customers use electricity in British Columbia, a statistically adjusted engineering (SAE) model was used to produce end use consumption estimates for nine end uses (lighting, refrigeration and freezing, process heat, pumps, fans and blowers, compressors, processes, materials handling and other). The study has three main results. First, understanding industrial end use consumption is important for energy load forecasting, DSM planning and DSM evaluation, and this study suggests that it may be an efficient and cost effective alternative to expensive end use metering. Second, to better understand the drivers of industrial energy consumption, four different models were estimated using ordinary least squares regression. All four models have good explanatory power with adjusted R-squared values of 0.98, and all coefficients have the appropriate signs and reasonable values. Third, end use estimates were made for the whole sample and for the three divisions: primary industry, secondary industry and tertiary industry. The average industrial facility in the total sample uses 13,951 MWh per year. The four largest uses for the total sample are other process (3,289 MWh per year), pumps (2,685 MWh per year), compression (1,859 MWh per year and fans (1,510 MWh per year). The average primary industry facility in the sample uses 4,052 MWh per year.

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