The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition
Clayton Miller,Anjukan Kathirgamanathan,Bianca Picchetti,Pandarasamy Arjunan,June Young Park,Zoltan Nagy,Paul Raftery,Brodie W. Hobson,Zixiao Shi,Forrest Meggers +9 more
TLDR
This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data that can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.Abstract:
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). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data were used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.read more
Citations
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Journal ArticleDOI
The ASHRAE Great Energy Predictor III competition: Overview and results
Clayton Miller,Pandarasamy Arjunan,Anjukan Kathirgamanathan,Chun Fu,Jonathan E. Roth,Jonathan E. Roth,June Young Park,Chris Balbach,Krishnan Gowri,Zoltan Nagy,Anthony Fontanini,Jeff Haberl +11 more
TL;DR: A high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps is given.
Journal ArticleDOI
Data science for building energy efficiency: A comprehensive text-mining driven review of scientific literature
TL;DR: The text-mining and NLP analysis reveals that data sciences techniques are applied more for operation phase applications such as fault detection and diagnosis (FDD), while being under-explored in design and commissioning phases.
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Assessment of long short-term memory and its modifications for enhanced short-term building energy predictions
TL;DR: Comparative evaluations of pure LSTM and five improved L STM models with modified structures were performed and the prediction performance of these models after parameter tuning was assessed in terms of prediction accuracy and computational time.
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Interpretable machine learning for building energy management: A state-of-the-art review
TL;DR: In this article , the authors present a review of previous studies that used interpretable machine learning techniques for building energy management to analyze how model interpretability is improved and discuss the future R&D needs for improving the interpretability of black-box models.
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Modelling a fifth-generation bidirectional low temperature district heating and cooling (5GDHC) network for nearly Zero Energy District (nZED)
TL;DR: The outcomes of this paper highlight the integration of established technologies into fifth-generation thermal networks, with a view to a future development of nearly Zero Energy Districts (nZED).
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