Predicting-energy-consumption

Part of WIDS datathon 2022

All the notebooks in this repository are possible solutions submitted to the WIDS (Women in Data Science) DATATHON 2022.

This year’s WiDS Datathon, organized by the WiDS Worldwide team, Stanford University, Harvard University IACS, and the WiDS Datathon Committee, are addressing an important way to mitigate the effects of climate change with a focus on energy efficiency. The WiDS Datathon Committee partnered with experts from many disciplines at Climate Change AI (CCAI), Lawrence Berkeley National Laboratory (Berkeley Lab), US Environmental Protection Agency (EPA), and MIT Critical Data.

Data

The WiDS Datathon 2022 focused on a prediction task involving roughly 100k observations of building energy usage records collected over 7 years and a number of states within the United States. The dataset consists of building characteristics (e.g. floor area, facility type etc), weather data for the location of the building (e.g. annual average temperature, annual total precipitation etc) as well as the energy usage for the building and the given year, measured as Site Energy Usage Intensity (Site EUI).

Datasets can be find in the following link of the competition:

https://www.kaggle.com/competitions/widsdatathon2022/data