Multi-features fusion for short-term photovoltaic power prediction

Intelligent and Converged Networks

article

Ming Ma¹  Xiaorun Tang²  Qingquan Lv²  Jun Shen³  Baixue Zhu²  Jinqiang Wang²  Binbin Yong² 

[1] State Grid Gansu Electric Power Research Institute;School of Information Science and Engineering,CHINA. Lanzhou University;School of Computing and Information Technology,AUSTRALIA. University of Wollongong

关键词: time series prediction;    meteorological factors;    multi-features fusion;    photovoltaic power prediction;   

DOI  :  10.23919/ICN.2022.0025

学科分类：社会科学、人文和艺术（综合）

来源: TUP

【 摘 要 】

In recent years, in order to achieve the goal of “carbon peaking and carbon neutralization”, many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic. However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction. This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China, and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation. Next, the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets. Finally, traditional time series prediction methods, such as Recurrent Neural Network (RNN), Convolution Neural Network (CNN) combined with eXtreme Gradient Boosting (XGBoost), are applied to study the impact of different feature fusion methods on power prediction. The results show that the prediction accuracy of Long Short-Term Memory (LSTM), stacked Long Short-Term Memory (stacked LSTM), Bi-directional LSTM (Bi-LSTM), Temporal Convolutional Network (TCN), and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological features. Therefore, the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.

【 授权许可】

CC BY-NC-ND