基于人工智能的客户需求响应研究与分析

随着电力大数据爆炸式地增长,依靠人力物力的传统数据处理模式已经不再适应现代电力系统的发展。以红河州地区居民用电消费行为和日常行为习惯做聚类分析,借助计量自动化系统提供的电力负荷数据,并基于用户群体分析与识别,利用灰色预测、BP神经元网络、自适应BP神经元网络、PSO算法、分类随机森林算法、自适应分类随机森林等人工智能算法对负荷进行预测。通过对当地居民的用电消费行为习惯深入的研究以及电力负荷的预测,为人工智能运用于电力系统客户需求响应提供理论经验和技术支撑。主要创新点在于将自适应随机森林算法等人工智能算法应用于居民需求响应的研究,降低电网处于高峰负荷,并采用了负荷转移策略降低电网运行成本,节约能耗。

Research and analysis of resident demand response based on artificial intelligence

With the explosive growth of power big data, the traditional data processing mode relying on a large amount of manpower and material resources is no longer suitable for the development of modern power systems. Cluster analysis is used to analyze the electricity consumption behavior and daily behaviors of residents in Honghe Prefecture. With the help of the power load data provided by the measurement automation system, and based on user group analysis and recognition, artificial intelligence algorithms such as gray prediction, BP neural network, adaptive BP neural network, PSO algorithm, classified random forest algorithm, and adaptive classification random forest are used to predict the load.Through in?depth research on the consumption habits of local residents and the prediction of power load, theoretical experience and technical support are provided for the application of artificial intelligence to power system customer demand response. The main innovation point is to apply the artificial intelligence algorithm such as adaptive random forest algorithm to the research of customer demand response, reduce the load of the grid during peak period, and adopt the load transfer strategy to reduce the grid operation cost and save energy.