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Title: Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models

This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-­building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a ``training period’’ and using the model to predict total electricity consumption during a subsequent ``prediction period.’’ We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-­performing model as judged by one metric was not the best performer when judged by another metric.
Authors:
 [1] ;  [1]
  1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.
Publication Date:
OSTI Identifier:
1237688
Report Number(s):
LBNL--187681
Journal ID: ISSN 0360-5442; ir:187681
Resource Type:
Journal Article
Resource Relation:
Journal Name: Energy (Oxford); Journal Volume: 66; Journal Issue: C
Publisher:
Elsevier
Research Org:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org:
Environmental Energy Technologies Division
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION