Please use this identifier to cite or link to this item:
https://doi.org/10.1080/23744731.2022.2067466
Title: | Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis | Authors: | MILLER, CLAYTON PICCHETTI, BIANCA FU, CHUN PANTELIC, JOVAN |
Keywords: | Science & Technology Physical Sciences Technology Thermodynamics Construction & Building Technology Engineering, Mechanical Engineering AUTOMATED MEASUREMENT CONSUMPTION VERIFICATION UNCERTAINTY LOAD SAVINGS MODELS METHODOLOGY PERFORMANCE ACCURACY |
Issue Date: | 3-May-2022 | Publisher: | TAYLOR & FRANCIS INC | Citation: | MILLER, CLAYTON, PICCHETTI, BIANCA, FU, CHUN, PANTELIC, JOVAN (2022-05-03). Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis. SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT 28 (5) : 10.1080/23744731.2022.2067466. ScholarBank@NUS Repository. https://doi.org/10.1080/23744731.2022.2067466 | Abstract: | Research is needed to explore the limitations and potential for improvement of machine learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was launched in 2019. This effort was the largest building energy meter machine learning competition of its kind, with 4370 participants who submitted 39,403 predictions. The test dataset included two years of hourly whole building readings from 2380 meters in 1448 buildings at 16 locations. This paper analyzes the various sources and types of residual model error from an aggregation of the competition’s top 50 solutions. This analysis reveals the limitations for machine learning using the standard model inputs of historical meter, weather, and basic building metadata. The errors are classified according to timeframe, behavior, magnitude, and incidence in single buildings or across a campus. The results show machine learning models have errors within a range of acceptability (RMSLEscaled = < 0.1) on 79.1% of the test data. Lower magnitude (in-range) model errors (0.1 < RMSLEscaled = < 0.3) occur in 16.1% of the test data. These errors could be remedied using innovative training data from onsite and Web-based sources. Higher magnitude (out-of-range) errors (RMSLEscaled > 0.3) occur in 4.8% of the test data and are unlikely to be accurately predicted. | Source Title: | SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT | URI: | https://scholarbank.nus.edu.sg/handle/10635/229407 | ISSN: | 23744731 2374474X |
DOI: | 10.1080/23744731.2022.2067466 |
Appears in Collections: | Staff Publications Elements |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
Limitations of machine learning for building energy prediction ASHRAE Great Energy Predictor III Kaggle competition error analysis (1).pdf | Published version | 4.2 MB | Adobe PDF | CLOSED | None |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.