Open AccessShort-term prediction model of photovoltaic power generation based on rough set-BP neural network
Author(s) -
Xinbin Ma,
Qingle Pang,
Qingsong Xie
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012010
Subject(s) - artificial neural network , computer science , data mining , rough set , outlier , cluster analysis , key (lock) , data set , term (time) , photovoltaic system , electric power system , set (abstract data type) , electricity generation , power (physics) , artificial intelligence , engineering , physics , computer security , quantum mechanics , electrical engineering , programming language
It is very important to predict the photovoltaic power generation because of the great challenges to the safe and stable operation of the power grid. Based on the analysis of traditional forecasting models, a power generation forecasting model based on rough set and neural network is proposed. Firstly, the Hampel filtering algorithm and rough set theory are used to process outliers and redundant data for the collected information. The above algorithms solve the problem of big data processing before forecasting. Finally, the fuzzy C-means clustering was used to divide the data set and combined with the neural network to predict. The key weather feature variables combined with the forecast time were taken as the input and the power was taken as the output variable. The results show that the prediction method proposed in this paper is more accurate and fast in predicting the power generation at each time.