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

高效准确的短期电力负荷预测对提升新型电力系统经济运行十分重要。针对极端天气场景下负荷预测数据量较少、随机性较强的特点,提出一种基于张量低秩补全算法的短期负荷预测模型,并选取极端高温场景展开研究。首先,给出极端天气定义,并基于改进型炎热指数和气温两项指标进行数据筛选;其次,提出一种基于张量的负荷数据补全模型,补全缺失数据;然后,通过Pearson相关性分析筛选输入特征量,构建基于长短时记忆(long short term memory, LSTM)网络和粗糙集理论(rough set theory, RST)的LSTM-RST短期负荷预测模型;最后,以苏州某地实际负荷数据设置算例进行验证,仿真结果表明,所提短期负荷预测方法具有较高的准确性。

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.