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

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.

References in corpus (2)

• The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
• The ASHRAE Great Energy Predictor III competition: Overview and results

Cited by in corpus (10)

• Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives
• The ASHRAE Great Energy Predictor III competition: Overview and results
• Using Google Trends as a proxy for occupant behavior to predict building energy consumption
• Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis
• Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data
• Creating synthetic energy meter data using conditional diffusion and building metadata
• Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads
• Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types
• The Building Data Genome Directory -- An open, comprehensive data sharing platform for building performance research
• ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles