论文标题
预测使用随机森林和梯度提升的短期能源消耗
Forecasting the Short-Term Energy Consumption Using Random Forests and Gradient Boosting
论文作者
论文摘要
本文相对分析了随机森林的性能和基于历史数据预测能源消耗的领域的梯度增强算法的性能。应用两种算法以单独预测能量消耗，然后使用加权平均合奏方法组合在一起。所达到的实验结果之间的比较证明，加权平均合奏方法比单独应用的两种算法中的每种都提供了更准确的结果。
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.
