Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms which make use of smart meter data. RAE contains 72 days of 1Hz data from a residential house 's mains and 24 sub-meters resulting in 6.2 million samples for each sub-meter . In addition to power data, environmental and sensor data from the house's thermostat is included. Sub-meter data  includes heat pump and rental suite captures which is of interest to power utilities. We also  show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.
Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms which make use of smart meter data. This initial release of RAE contains 1Hz data (mains and sub-meters) from two a residential house . In addition to power data, environmental and sensor data from the house's thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures which is of interest to power utilities. We also show and energy breakdown of each house and show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.
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RAE contains 72 days of
This initial release of RAE contains
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from
(mains and sub-meters) from two
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's mains and 24 sub-meters resulting in 6.2 million samples for each sub-meter
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from one of the houses
A
show and energy breakdown of each house and