Data Analytics for Electricity Revenue Forecasting by using Linear Regression and Classification Method

This research focused on which factors affect electricity sales revenue and how much it costed. We used data mining: linear regression and classification method to analyze with RapidMiner Studio Tool. In this case, the result shows that classification model is more accurate than linear regression. The population is the most important factor to forecast the revenue. GDP is also significant, but it is not all the case. The amount of price in residential, commercial, and industrial sector affect the revenue as well.