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

TL;DR

This paper introduces an extensive open dataset of energy meter readings from over 1,600 non-residential buildings, used in a major machine learning competition, enabling research in energy prediction, anomaly detection, and building analysis.

Contribution

It provides a comprehensive, cleaned, and well-documented dataset from diverse buildings, facilitating benchmarking and novel research in building energy analysis.

Findings

Dataset includes 53.6 million measurements across various energy types.

Data was used in the ASHRAE GEPIII competition for energy prediction.

The dataset supports tasks like anomaly detection and building 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.