Description

This paper presents a novel approach used to develop the U.S. Department of Energy (DOE) commercial building energy asset rating tool. Asset rating, a national standard for a voluntary energy rating system, is intended to help building owners better understand the installed system performance and the total energy use. The asset rating tool allows users to benchmark their buildings against peers and other market players to understand the relative efficiency of different buildings in a way that is distinct from their operations and occupancy. A simplified data collection integrated with full-scale energy-modeling method is employed to disaggregate building energy information and will include a mechanism for identifying energy improvement opportunities. A more detailed modeling approach to formulate an asset rating would most likely provide the greatest and accuracy; while a simplified model approach requires less user investment for collecting data. However, our market research suggested that an asset rating program needs to consider not only the applicability and accuracy across the breadth of commercial buildings but also ease of use. To take the above design drivers into account, we are developing an asset rating tool, a web-based application with a simplified user interface built on an inference engine and a centralized modeling engine. The method presented in this paper separates model inputs into categories based on their overall energy impact, difficulty level of data collection, and variability among buildings. We outline an approach that will allow great flexibility in terms of how many and which of the different categories of variables must be found to produce an accurate energy model. The approach will allow all key variables to be inferred from some reduced set of variables while at the same time allowing a user to enter many more variables if he or she has reliable details on them. The asset rating tool is not just a rating tool, but is aimed at providing a cost-effective means for building owners and operators to gain insight into the energy efficiency potential of their buildings. The development of such a tool enables reduced modeling time and expertise requirements while maintaining accuracy and the ability to support the variability and complexity that exist in buildings.

Product Details

Published:

2012

Number of Pages:

11

File Size:

1 file , 400 KB

Product Code(s):

D-SA-12-C023

Note:

This product is unavailable in Russia, Belarus