|Title||Development and Application of a Statistical Methodology to Evaluate the Predictive Accuracy of Building Energy Baseline Models|
|Year of Publication||2013|
|Authors||Jessica Granderson, Phillip N Price|
|Institution||Lawrence Berkeley National Laboratory|
|Keywords||baseline model, energy savings, Measurement and verification, performance accuracy, prediction, whole-‐building energy|
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.
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