The RICO dataset: A multivariate HVAC indoors and outdoors time-series dataset

Indoor temperature forecasting is an area of interest and importance as it contributes to improving HVAC systems control, thus reducing wasted energy and improving health and comfort. Acquiring high-quality transitory regime data for training Machine Learning models is challenging due to the scarcity of publicly available dataset. Additionally, such a dataset acquisition incurs high costs from repeated heating and cooling buildings in ranges of temperatures that go beyond normal operation thresholds. In response, we propose an open-source dataset called 'RICO Dataset'. It is acquired in a dedicated and controlled physical test-building, alleviating potential issues encountered by digital simulation and modelling. It contains 305, four hours long 80-features rich multivariate transitory time series data from sensors in both internal and external environments sampled at a rate of one per minute.