Prediction of Hourly Household Power Consumption

With the recent increase in smart meters across the residential sectors, we have large publicly available datasets. With such data, the power consumption of individual households can be tracked in almost real-time.

Such prediction can help power companies regulate their supply; also, the consumer can use this information to make better decisions both financially and environment-consciously.

In this project, we address this challenge by trying 4 different Machine learning Algorithms to do a comparative analysis to see which approach works best.

The 4 approaches used are:

• Auto Regression (AR Model)
• Support Vector Regression
• Linear Regression
• Random Forest

let’s move our…