Time series using XGBOOST for predicting energy consumption

MLP 10 - Time series using XGBOOST for predicting energy consumption

Industry Energy sector| Utilities

Skills Python | Time series | Data manipulation | XGBoost

Problem statements Predict energy consumption.

Data Structure PJM Hourly Energy Consumption Data PJM Interconnection LLC (PJM) is a regional transmission organization (RTO) in the United States. It is part of the Eastern Interconnection grid operating an electric transmission system serving all or parts of Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia. The hourly power consumption data comes from PJM's website and are in megawatts (MW). The regions have changed over the years so data may only appear for certain dates per region.

Methods Train/test split data Create time series features based on time series index. 'hour, 'dayofweek', 'quarter','month','year','dayofyear','dayofmonth' and 'weekofyear' Visualize our Feature / Target Relationship using boxplots (hr of the day vs energy consumption and month vs energy consumption) Create model with XGBoost regressor and set parameters Fit model to training dataset Investigate feature importance with bar plot Investigate ground through vs predicted values Validate model accuracy with RMSE Score on Test set Calculate the error of prediction by looking at the worst and best predicted days

Results Month and hour influences energy consumption the most
Most energy consumed during winter months Most energy consumed during early morning hours Increase model accuracy with fine tuning model Increasing model accuracy by adding public holidays to model considerations