Jhanavi Hegde

How can we create an energy prediction model of prosumers to reduce energy imbalance costs?

Started Nov. 1, 2023

Abstract or project description

Through multiple Kaggle learning courses, I was able to improve my understanding of machine learning, specifically aspects of it that were relevant to the Enefit challenge. The Enefit challenge is a Kaggle challenge with the goal of creating an energy prediction model of prosumers (both producers and consumers of energy) to reduce energy imbalance costs. This challenge poses a very real situation that has been propelling global warming discussions in recent years: How can we reduce energy usage, and ultimately, fossil fuel consumption? By participating in this challenge, I hope to have the opportunity to apply the skills I have learned in the past and in Kaggle learning courses to a real-world issue. I also hope to demonstrate my learning journey through a final notebook that contains both my code, multiple comments explaining my work, and a reflection. Through my notebook, I would like to share my insights with other programmers around the world and contribute to collective knowledge in data science with new features or methods for developing models and analyzing data.