# RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis

## TL;DR

The RAE dataset provides high-resolution residential smart meter and environmental data to facilitate the development and testing of machine learning models for energy consumption analysis and non-intrusive load monitoring.

## Contribution

This paper introduces the RAE dataset, a new comprehensive dataset with power, environmental, and sensor data for smart grid research and algorithm testing.

## Key findings

- Contains 1Hz power and environmental data from two houses.
- Includes sub-meter data for heat pumps and rental units.
- Demonstrates use of RAE for testing NILM algorithms.

## Abstract

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.