Research on medium and long term load forecasting method in courts based on K-means clustering and random forest

J. Luo, Dongtao Wang
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Abstract

With the rapid development of China's economy, people's power consumption level has gradually improved, which has brought great pressure to the distribution and power supply in the distribution station area. Accurate load forecasting of distribution station area provides a reference basis for capacity expansion planning of distribution station area. This paper comprehensively considers the self factors and external influencing factors of the distribution station area, carries out cluster division according to its geographical location and maximum load, analyzes the power consumption behavior of different types of distribution station areas, and establishes the maximum load model of different types of distribution station areas by using the random forest regression cycle, so as to improve the prediction accuracy. This method overcomes the disadvantages of large difference in load data in distribution station area and difficult to quantify.
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基于k均值聚类和随机森林的法院中长期负荷预测方法研究
随着中国经济的快速发展,人们的用电水平逐渐提高,这给配电站区域的配电和供电带来了很大的压力。准确的配电站区负荷预测为配电站区扩容规划提供了参考依据。综合考虑配电站区域自身因素和外部影响因素,根据其地理位置和最大负荷进行聚类划分,分析不同类型配电站区域的用电行为,利用随机森林回归周期建立不同类型配电站区域的最大负荷模型,以提高预测精度。该方法克服了配电站区负荷数据差异大、难以量化的缺点。
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