According to a report issued by the International Energy Agency (IEA), the lifecycle of buildings from construction to demolition was responsible for 37% of global energy-related and process-related CO2 emissions in 2020. Yet it is possible to drastically reduce the energy consumption of buildings by a combination of easy-to-implement fixes and state-of-the-art strategies.

The dataset consists of building characteristics, weather data for the location of the building, as well as the energy usage for the building, and the given year, measured as Site Energy Usage Intensity (Site EUI). Each row in the data corresponds to a single building observed in a given year.

The two datasets: (1) the train_dataset where the observed values of the Site EUI for each row exists but not provided here due to huge size and (2) the x_test dataset the observed values of the Site EUI for each row are removed and provided separately in y_test. The task was to predict the Site EUI for each row (using the complete training dataset), given the characteristics of the building and the weather data for the location of the building.

The target variable is site_eui and the evaluation metric is RMSE score.