Steel-Energy-Consumption-Prediction

The dataset retrieved from UCI's machine learning repository. I have used Random Forest and XGBoost Regressors in order to predict the energy consumption for the DAEWOO Steel Co. This project is based on Sathishkumar's V E, research.

The results of the models was impressive and the first thought was the presence of overfitting, after an investigation I realized that the data structure and high correlation between the features was responsible for high scores. High data quality matters!

Relevant Papers:

1. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city�, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.
2. Sathishkumar V E, Myeongbae Lee, Jonghyun Lim, Yubin Kim, Changsun Shin, Jangwoo Park, Yongyun Cho, “An Energy Consumption Prediction Model for Smart Factory using Data Mining Algorithms� KIPS Transactions on Software and Data Engineering, Vol. 9, no. 5, pp. 153-160, 2020. Transactions on Software and Data Engineering, Vol. 9, no. 5, pp. 153-160, 2020.
3. Sathishkumar V E, Jonghyun Lim, Myeongbae Lee, Yongyun Cho, Jangwoo Park, Changsun Shin, and Yongyun Cho, “Industry Energy Consumption Prediction Using Data Mining Techniques�, International Journal of Energy Information and Communications, Vol. 11, no. 1, pp. 7-14, 2020.