基于梯度提升树计及非线性的电力负荷影响因素分析

分析负荷影响因素对电力负荷的影响对于电网调度人员了解负荷特性，提高负荷预测准确度具有重要的意义。针对传统相关性分析方法不能考虑复杂非线性影响的问题，采用先训练负荷预测模型，再分析相关性的思路，提出基于负荷预测模型的相关性分析方法，发现两者之间的非线性相关关系。首先，利用梯度提升树(Gradient Boosting Decision Tree, GBDT)的非线性建模和特征提取能力训练负荷预测模型。然后，基于预测模型提出采用重要性衡量影响因素对负荷的非线性影响，识别重要影响因素。最后，利用负荷对影响因素的偏依赖量计算各类影响因素变化对负荷变化趋势的非线性影响。采用实际的负荷数据进行验证，并与皮尔逊相关系数法进行对比。实验结果表明该方法能够有效识别影响负荷的重要因素，并能够发现各类因素和负荷之间的非线性关系。

Nonlinear correlation analysis of influence factors of a power load based on a gradient boosting decision tree

Analyzing the influence of factors on a power load is important for power grid dispatchers to understand load characteristics and improve the accuracy of load forecasting. The traditional correlation analysis method cannot consider complex nonlinear effects, so a new method, which can find the nonlinear correlation, is proposed to analyze the correlation of factors and power load. First, this paper uses the learning ability of the gradient boosting decision tree to train a power load forecasting model. Then, based on the forecasting model, it proposes the importance of measuring the nonlinear correlation between factors and power load. Finally, the partial dependence of power load on the factors is used to calculate the nonlinear effects of influencing factor changes on the trend of load changes. The real power load is used for verification and compared using the Pearson correlation coefficient. The experimental results show that the method can effectively identify the important factors which affect power load, and can find the nonlinear relationship between various factors and the power load.This work is supported by Science and Technology Project of State Grid Corporation of China (No. 5700-202055368A-0-0-00).