Day-ahead power forecasting of distributed photovoltaic generation based on weighted expanded daily feature matrix

被引：0
|
作者：
Zheng R. [1 ]
Li G. [1 ]
Han B. [1 ]
Wang K. [1 ]
Peng D. [2 ]
机构：
[1] Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai
[2] School of Automation Engineering, Shanghai University of Electric Power, Shanghai
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2022年 / 42卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Correlation; Daily feature matrix; Day-ahead power forecasting; Distributed photovoltaic; Neural network; Similar day;
D O I：
10.16081/j.epae.202112023
中图分类号：
学科分类号：
摘要：
Accurate day-ahead power forecasting of distributed household photovoltaic generation system can provide a basis for optimal operation of smart houses, but the problems of lack of historical data and precise irradiance forecasting data increase forecasting difficulty. Therefore, the sample scale is enlarged by integra-ting data from multiple users in the nearby area, a similar day selection method considering power correlation and relevant weight is proposed, and the day-ahead forecasting is realized based on LSTM(Long Short-Term Memory) neural network. The influencing factors of photovoltaic generation power and their internal correlation are analyzed, the day types are classified based on the statistical data of weather type, and the meteorological information, historical power information and Pearson product-moment correlation coefficient are used to construct the weighted expanded daily feature matrix. The photovoltaic power of similar day with minimum Euclidean distance of feature matrix of the day to be forecasted is selected from historical data, and it is input LSTM neural network model together with key meteorological features for forecasting. The validity of the proposed method is verified by the measured data of multiple users in Denver City of North America, the proposed method can be applied in the scene with limited historical data and can signifi-cantly reduce the forecasting error in multiple weather types. © 2022, Electric Power Automation Equipment Press. All right reserved.
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页码：99 / 105
页数：6
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