ST-16-C008 -- Gaussian Process Baseline Regression Models in Industrial Facilities ✓ Most Recent

This study analyzes the effectiveness of Gaussian Process (GP) modeling for establishing baseline energy usage models in industrial facilities using utility bill data. Several different methods of creating baseline models for commercial and residential buildings have been developed; however, few attempts have been made to create baseline energy models in industrial facilities. Industrial facilities account for 33% of annual energy usage within the United States, so industrial energy usage needs to be analyzed in order to identify energy saving opportunities. Creating a baseline energy model is key to understanding an industrial facility's energy usage. Currently, only change-point regression models have been used for analysis in industrial facilities. An analysis of the effectiveness of using (GP) regression (to develop a baseline energy usage models in industrial facilities from utility bill data and ambient outdoor temperature is presented. Two case studies are presented: using utility bill data to create a GP regression. In both cases the baseline regression models gave a CV-RMSE of 15% or lower and NMBE of 5% or lower showing that a GP regression model using utility bill data is capable of producing accurate baseline energy models.