Model14

We have followed the three-step approach to tackle this challenge.Data preparation – We started with the aggregation of the energy consumption data at a daily level (originally given at a half-hourly interval). Missing values were observed in temperature metadata and consumption data, which we imputed using daily level linear interpolation.Next was feature engineering which was essential to bring out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data – used as is (we have used avg. weather data only)b) Time-Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon, Tue,……Sun)3. WeekofYear(1,2,…52)c) Cyclic encoding of periodic features for the month and weekday1. Month gets mapped to Month_x = sin(2*pi*month/12) and Month_y=cos(2*pi*month/12)2. Weekday gets mapped to weekday_x = sin(2*pi*weekday/6) and weekday_y=cos(2*pi*weekday /6).Post multiple iterations, we settled with the XgBoost model since it performed the best among all. We trained the model on individual meter_ID and forecasted energy consumption at a daily level. The outcome was then rolled-up, to a monthly level to get our final submission.For the customer having one month of data, we performed fuzzy clustering of time series using December daily consumption data then daily consumption data for the whole year for missing months were imputed using the cluster's average consumption and the probability of meter-id to fall in each cluster and took summation.