Ultra‐short‐term irradiance forecasting model based on ground‐based cloud image and deep learning algorithm

Autor: Zhao Zhen, Xuemin Zhang, Shengwei Mei, Xiqiang Chang, Hua Chai, Rui Yin, Fei Wang
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IET Renewable Power Generation, Vol 16, Iss 12, Pp 2604-2616 (2022)
Druh dokumentu: article
ISSN: 1752-1424
1752-1416
DOI: 10.1049/rpg2.12280
Popis: Abstract Solar irradiance is the main factor affecting the output of a photovoltaic (PV) power station, which is chiefly determined by the cloud distribution over the power station. For ultra‐short‐term, especially the intro‐hour time scale irradiance forecasting, ground‐based cloud image is considered as a very necessary data as Global Horizontal Irradiance (GHI). However, the information content in the image is much higher than that of GHI record, and there is even a difference in magnitude between them. Therefore, how to effectively extract the key features in the cloud images and fuse them with GHI record data is the decisive factor affecting the performance of the forecasting model. Here, a novel convolutional auto‐encoder based cloud distribution feature (CDF) extraction method is first proposed. Then for feature fusion part, an LSTM‐FUSION irradiance forecasting model is established based on long short‐term memory (LSTM) neural network and feature fusion by time steps considering the one‐to‐one correlation between CDFs and GHI. Finally, a novel determination method of input time step length based on attention distribution analysis is also proposed. Simulation results show that the proposed LSTM‐FUSION model is overall superior to the benchmark models.
Databáze: Directory of Open Access Journals
Externí odkaz: