Introduction & Outline: Assessing the value of energy efficiency improvements can be challenging as there's no way to truly know how much energy a building would have used without the improvements. The best we can do is to build counterfactual models. Once a building is overhauled the new (lower) energy consumption is compared against modeled values for the original building to calculate the savings from the retrofit. More accurate models could support better market incentives and enable lower cost financing.
This Kaggle competition challenges you to build these counterfactual models across four energy types based on historic usage rates and observed weather. The dataset includes three years of hourly meter readings from over one thousand buildings at several different sites around the world.
In this project, we'll follow a step-by-step process for building the ML model:
- Download the dataset
- Exploring the dataset
- Prepare the dataset for training
- Train & Evaluate Different Models
Check out the jupyter notebook here: https://jovian.ai/prasanthi-vvit/ashrae-energy-prediction