Change point and degree day baseline regression models in industrial facilities

Abstract Industrial facilities account for 33% of the annual energy usage within the United States [17] and this large sector of the domestic energy budget presents significant opportunities for energy efficiency. In order to effectively analyze energy savings opportunities in industrial facilities, adequate baseline models of energy usage in the facilities are needed. Multiple methods of creating baseline energy models have been used in commercial and residential buildings, however, few of these techniques have been made applied to develop baseline energy models in industrial facilities. The paper investigates the application of standard regression models used for commercial and residential buildings to industrial facilities using sparse energy consumption data. An analysis of the effectiveness of three parameter cooling (3PC) and cooling degree day (CDD) regression models to develop baseline energy usage models in industrial facilities from commonly available utility bill data is presented. Two case studies are investigated: in both case studies a comparison between 3PC model and CDD model is presented. In both cases the baseline regression models meet the recommended NMBE from the ASHRAE Guideline 14. A method to determine process equipment energy usage and cooling end use due to internal loads is presented.

[1]  David E. Claridge,et al.  Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings , 1998 .

[2]  J. Kelly Kissock,et al.  Measuring industrial energy savings , 2008 .

[3]  Margaret F. Fels PRISM: An Introduction , 1986 .

[4]  Jeffrey S. Simonoff,et al.  Handbook of Regression Analysis , 2012 .

[5]  Dan Brown,et al.  Estimating Industrial Building Energy Savings using Inverse Simulation , 2011 .

[6]  John Seryak,et al.  UNDERSTANDING MANUFACTURING ENERGY USE THROUGH STATISTICAL ANALYSIS , 2004 .

[7]  Lawrence C. Marsh Spline Regression Models , 2001 .

[8]  David E. Claridge,et al.  Baselining methodology for facility-level monthly energy use - Part 2: application to eight army installations , 1997 .

[9]  Allyson Katherine Golden Analyzing industrial energy use through ordinary least squares regression models , 2014 .

[10]  H. Sung Gaussian Mixture Regression and Classification , 2004 .

[11]  Zheng O'Neill,et al.  A COMPARISON OF GAUSSIAN PROCESS REGRESSION AND CHANGE-POINT REGRESSION FOR THE BASELINE MODEL IN INDUSTRIAL FACILITIES , 2016 .

[12]  Abhishek Srivastav,et al.  Baseline building energy modeling and localized uncertainty quantification using Gaussian mixture models , 2013 .

[13]  Steven C. Chapra,et al.  Applied Numerical Methods with MATLAB for Engineers and Scientists , 2004 .

[14]  David E. Claridge,et al.  Use of Simplified System Models to Measure Retrofit Energy Savings , 1993 .

[15]  Victor M. Zavala,et al.  Gaussian process modeling for measurement and verification of building energy savings , 2012 .

[16]  Zheng O'Neill,et al.  Comparisons of inverse modeling approaches for predicting building energy performance , 2015 .

[17]  Kelly Kissock,et al.  Understanding Industrial Energy Use through Lean Energy Analysis , 2011 .

[18]  E. Lawrence Energy Efficiency Improvement and Cost Saving Opportunities for the Vehicle Assembly Industry , 2008 .

[19]  Pingfang Hu,et al.  A Baseline Model for Office Building Energy Consumption in Hot Summer and Cold Winter Region , 2009, 2009 International Conference on Management and Service Science.

[20]  J. Kelly Kissock,et al.  Targeting Residential Energy Assistance , 2007 .