Forecasting the Short-Term Energy Consumption Using Random Forests and Gradient Boosting
This is an incremental improvement for energy management systems, offering a specific method to enhance forecasting accuracy.
The paper tackled short-term energy consumption forecasting by comparing Random Forests and Gradient Boosting, finding that a Weighted Average Ensemble Method combining them provided more accurate results than either algorithm alone.
This paper analyzes comparatively the performance of Random Forests and Gradient Boosting algorithms in the field of forecasting the energy consumption based on historical data. The two algorithms are applied in order to forecast the energy consumption individually, and then combined together by using a Weighted Average Ensemble Method. The comparison among the achieved experimental results proves that the Weighted Average Ensemble Method provides more accurate results than each of the two algorithms applied alone.