分布式能源2024，Vol.9Issue(4)：51-59,9.DOI:10.16513/j.2096-2185.DE.2409406
基于张量低秩补全算法的极端天气短期负荷预测
Short-Term Load Forecasting Based on Tensor Low-Rank Completion Algorithm in Extreme Weather
摘要
Abstract
Efficient and accurate short-term power load forecasting is very important to improve the economic operation of the new power system.In view of the characteristics of less load forecasting data and strong randomness in extreme weather scenarios,a short-term load forecasting model based on the tensor low-rank completion algorithm is proposed,and extreme high temperature scenarios are selected for the research.First,the definition of extreme weather is given and data screening is performed based on the improved heat index and temperature.Then,a tensor-based load data completion model is proposed to complete the missing data.The input features are selected by Pearson correlation analysis,and the short-term load forecasting model based on long and short time memory(LSTM)network and rough set theory(RST)is constructed.Finally,the actual load data in Suzhou is used for verification,and the simulation results show that the proposed short-term forecasting method has high accuracy.关键词
极端天气/高温场景/炎热指数/短期负荷预测/张量低秩补全/长短时记忆(LSTM)网络/粗糙集理论(RST)Key words
extreme weather/high temperature scenario/heat index/short-term load forecasting/tensor low-rank completion/long short term memory(LSTM)network/rough set theory(RST)分类
能源科技引用本文复制引用
冯家欢,史雪晨,张赟,胡涛,封钰,洪晨威,洪奕,吴越涛..基于张量低秩补全算法的极端天气短期负荷预测[J].分布式能源,2024,9(4):51-59,9.基金项目
国家电网公司科技项目(5100-202235272A-2-0-XG) This work is supported by Science and Technology Project of State Grid Corporation of China(5100-202235272A-2-0-XG) （5100-202235272A-2-0-XG）
