文章基本信息

标题：Ultra-short-term Load Forecasting Based on Real-Time Response of Classified Flexible Loads
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作者：Dunnan Liu ; Pengfei Li ; Xiaofeng Xu 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2020
卷号：165
页码：1-4
DOI：10.1051/e3sconf/202016503048
出版社：EDP Sciences
摘要：Ultra-short-term load forecasting is an important basis for optimization and adjustment of power generation plans and dispatch plans. Based on the radial basis function neural network, the inert load is predicted, and the flexible load is predicted based on the price elasticity of electricity demand. Then, combined with the range of the flexible load, an ultra-short-term forecast interval for the total load is constructed. This paper studies the total load after considering the flexible load for demand response, and verifies the feasibility of the proposed method with an example.