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These locations, climates, and the number of buildings from each site are found in Table 1. This data set was collected from 19 different locations from around the world. It can also be used merely as a teaching or learning tool to practice dealing with measured performance data from large numbers of non-residential buildings. This data set can be used to benchmark various statistical learning algorithms and other data science techniques. Each of the buildings has metadata such as area, weather, and primary use type collated. The hourly frequency for the data set was targeted as it provides enough resolution to support analytics techniques targeting several scales, including daily, weekly, monthly, seasonal, and annual patterns of use. It includes hourly whole-building data for two years, from different kinds of meters: electricity, chilled water, steam, hot water, gas, water, irrigation, and solar. This open data repository has data from 1,636 non-residential buildings. The newest version of the data set is described in this publication as the Building Data Genome Project 2 data set. The first phase of the project had a data set that was released in 2017 and included one year of hourly data from over 500 buildings 16. The data set is part of the Building Data Genome Project, an international consortium of building energy-related academics and practitioners who seek to create large, open data sets that increase the understanding of the foundations of building behavior and energy use in buildings. This paper focuses on the development of a data set that builds upon these motivations. From the machine learning perspective, there have also been efforts towards using large data sets to benchmark various machine learning techniques as applied to building energy performance analytics 15.

The use of open data sets in the built environment enables the analysis of large numbers of buildings in applications such as benchmarking 14. Several prominent open building energy-related data sets have been released in recent years including applications to building-level office 6 and residential 7 appliances, occupant behavior 8, heat pump 9 and natural ventilation systems 10, as well as commercial and residential energy meter data 11, 12, 13. The building energy analytics community has only just started to use open data sets towards the efforts of creating benchmarking data sets.

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This statement is supported by the significant efforts in time-series data classification 3, image recognition 4, and the larger machine learning community in general, both hardware and software 5.

When it comes to machine learning innovation in academia, one of the most significant assets can be large and open data sets that the community can use to prototype and quantitatively compare techniques in ways that show better value in terms of speed, accuracy, or implementation ease.

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Despite the significant research body of knowledge developed, there is still a lack of understanding of how to scale techniques across the highly heterogeneous building stock 2.

Machine learning and prediction techniques are a vital component of many of the ways of finding savings opportunities and quantifying the risk and reward of undertaking such efforts.

This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.īuilding performance analytics and commissioning processes have significant opportunities to save energy, reduce carbon emissions of buildings, and reduce the operating costs of building owners world-wide 1. 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. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. 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.

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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 (20) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements).

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