基于最优特征和改进随机森林的非侵入式负荷辨识方法

非侵入式负荷监测方法是实现电网智能化的关键技术，有助于优化能量管理，促进能源高效利用。为了应对目前大多数负荷辨识模型存在的特征冗余、识别精度有限和计算效率低下等问题，提出了一种基于最优特征和改进随机森林的新型非侵入式负荷辨识方法。首先，通过递归特征消除方法从众多负荷特征中自主确定最优特征组合，以减少信息冗余。然后，确定不同决策树的权重数值，通过构建加权随机森林模型来实现电器负荷的辨识。为了进一步提高算法的精确度，利用改进鲸鱼算法对随机森林的重要参数进行优化。最终在公开数据集进行实验验证，证明所提负荷辨识方法具有准确性和优越性。

Non-intrusive load identification method based on optimal signatures and improved random forest

Non-intrusive load monitoring method is a critical technology for realizing power system intelligence,which helps to optimize energy management and promote efficient energy utilization. In order to cope with the existing problems of feature redundancy,limited recognition accuracy and computational inefficiency,a novel non-intrusive load identification method based on optimal signatures and improved random forest is proposed. Firstly,the optimal feature combination is autonomously determined by recursive feature elimination method to reduce the information redundancy. Then load identification is realized by constructing a weighted random forest model. The weights are established by utilizing out-of-bag data. Construction parameters of random forest are optimized using the improved whale algorithm. Ultimately,the experimental results prove the accuracy and superiority of the proposed load identification method.