Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.apenergy.2020.115413
Title: | EnergyStar plus plus : Towards more accurate and explanatory building energy benchmarking | Authors: | Arjunan, Pandarasamy Poolla, Kameshwar Miller, Clayton |
Keywords: | Building energy benchmarking Building performance rating Multiple linear regression Gradient boosting trees Feature interaction Interpretable machine learning |
Issue Date: | 15-Oct-2020 | Publisher: | ELSEVIER SCI LTD | Citation: | Arjunan, Pandarasamy, Poolla, Kameshwar, Miller, Clayton (2020-10-15). EnergyStar plus plus : Towards more accurate and explanatory building energy benchmarking. APPLIED ENERGY 276. ScholarBank@NUS Repository. https://doi.org/10.1016/j.apenergy.2020.115413 | Abstract: | Building energy performance benchmarking has been adopted widely in the USA and Canada through the Energy Star Portfolio Manager platform. Building operations and energy management professionals have long used a simple 1-100 score to understand how their building compares to its peers. This single number is easy to use, but is created by inaccurate linear regression (MLR) models. This paper proposes a methodology that enhances the existing Energy Star calculation method by increasing accuracy and providing additional model output processing to help explain why a building is achieving a certain score. We propose and test two new prediction models: multiple linear regression with feature interactions (MLRi) and gradient boosted trees (GBT). Both models have better average accuracy than the baseline Energy Star models. The third order MLRi and GBT models achieve 4.9% and 24.9% increase in adjusted R2, respectively, and 7.0% and 13.7% decrease in normalized root mean squared error (NRMSE), respectively, on average than MLR models for six building types. Even more importantly, a set of techniques is developed to help determine which factors most influence the score using SHAP values. The SHAP force visualization in particular offers an accessible overview of the aspects of the building that influence the score that non-technical users can readily interpret. This methodology is tested on the 2012 Commercial Building Energy Consumption Survey (CBECS)(1,812 buildings) and public data sets from the energy disclosure programs of New York City (11,131 buildings) and Seattle (2,073 buildings). | Source Title: | APPLIED ENERGY | URI: | https://scholarbank.nus.edu.sg/handle/10635/189365 | ISSN: | 03062619 18729118 |
DOI: | 10.1016/j.apenergy.2020.115413 |
Appears in Collections: | Elements Staff Publications |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
1910.14563v2.pdf | Accepted version | 973.66 kB | Adobe PDF | OPEN | Post-print | View/Download |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.