Search Results for author: Bianca Picchetti

Found 2 papers, 2 papers with code

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

1 code implementation • 25 Jun 2021 • Clayton Miller, Bianca Picchetti, Chun Fu, Jovan Pantelic

Higher magnitude (out-of-range) errors (RMSLE_scaled > 0. 3) occur in 4. 8% of the test data and are unlikely to be accurately predicted.

BIG-bench Machine Learning

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The Building Data Genome Project 2: Hourly energy meter data from the ASHRAE Great Energy Predictor III competition

2 code implementations • 3 Jun 2020 • Clayton Miller, Anjukan Kathirgamanathan, Bianca Picchetti, Pandarasamy Arjunan, June Young Park, Zoltan Nagy, Paul Raftery, Brodie W. Hobson, Zixiao Shi, Forrest Meggers

This paper describes an open data set of 3, 053 energy meters from 1, 636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17, 544 measurements per meter resulting in approximately 53. 6 million measurements).

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