Nowcasting the next hour of residential load using boosting ensemble machines

Accurate residential load forecasting is a key to achieve grid stability and efficient energy management. However, it becomes challenging due to the non-linear and seasonally fluctuating energy usage of domestic users. Existing statistical and machine learning-based forecasting models struggle to produce accurate forecasts due to dynamic and stochastic user behaviors for energy usage. On the other hand, pairwise ensemble methods can achieve higher forecasting accuracy in short-term load forecasting, but are not scalable and face generalization issues that often lead to overfitting and complexity in managing multivariate data. To address these limitations, we propose to integrate LightGBM, XGBoost and CatBoost models to systematically address the limitations of existing ensemble-based forecasting models. This integration aims to leverage the strengths of each ensemble method, where LightGBM handles generalization across multiple sites, XGBoost avoids overfitting the model, and CatBoost effectively manages categorical features. We implement our proposed model using a real-world, publicly available dataset for 13 residential locations in North America and Europe. The proposed model outperforms other state-of-the-art algorithms with the lowest root mean squared logarithmic error (RMSLE) values of 0.1898, while the coefficient of determination (R²) calculated from the data is 0.9745. Other evaluation measures such as root mean square error (RMSE), coefficient of variation of the root mean square error (CVRMSE) and mean bias error (MBE) also support the proposed approach regarding the efficiency of the model.Finally, we also perform an ablution study to show predictive efficacy of incremental model integration.