一种Stacking集成结构的台风灾害下停电空间预测方法

为提高电网公司防灾减灾能力,考虑电网、气象、地理等因素,从统计学习的角度提出一种台风灾害下停电空间预测方法。首先,以1 km×1 km网格为单位收集数据,并进行标准化、分类变量独热编码处理与筛选、构造特征等处理后作为模型输入数据。其次,选取随机森林、梯度提升决策树、自适应提升、K最近邻、支持向量机、极限树、决策树以及XGBoost等算法,利用Stacking集成技术构造停电空间预测模型。最后,以广东省某县为研究对象,对模型的停电空间预测性能进行验证。在台风“彩虹”下的预测准确率为0.7776,召回率为0.9140。结果验证了在台风灾害下该模型对停电空间预测的可行性与有效性。

A space prediction method for power outage in a typhoon disaster based on a Stacking integrated structure

To improve disaster prevention and the mitigation ability of power grid companies, taking into account the power grid, meteorology and geography, a power outage spatial prediction method in the case of typhoon disasters is proposed from the perspective of statistical learning. First, data are collected in a 1 km×1 km grid, and standardized, classified with variable one-hot encoding and screening, and construction features are processed as input data for the model. Secondly, algorithms such as random forest, gradient boosting decision tree, adaptive boosting, K-nearest neighbor, support vector machine, extreme tree, decision tree, and XGBoost are selected to construct an outage space prediction model using Stacking integration technology. Finally, a city in Guangdong Province is taken as the research object to verify the performance of the model: the prediction accuracy rate for typhoon "Mujigae" is 0.777 6, and the recall rate is 0.914 0. The results verify the feasibility and effectiveness of the model in predicting power outage space in the case of typhoon disasters. This work is supported by the University-Industry Collaborative Education Program of the Ministry of Education (No. 201902056044).