TY - GEN
T1 - Comparison between GRU and BP Neural Networks for Short-Term Prediction of Solar Irradiance
AU - Zhenzhen, Zhou
AU - Yunhai, Song
AU - Sen, He
AU - Heyan, Huang
AU - Yuhao, He
AU - Shaohui, Zhou
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this study, we present a data-driven approach for predicting solar irradiance by applying dimensionality reduction techniques to a dataset comprising of ground meteorological station data and FY-4A remote sensing data collected from January 1st to December 31st, 2018. Specifically, we use Principal Component Analysis (PCA) to reduce the dimensionality of the dataset. Next, we employ a Gated Recurrent Unit (GRU) neural network-based short-term irradiance prediction model to realize the short-term prediction of solar irradiance. We then evaluate the performance of the GRU model by comparing its prediction results with those obtained using a traditional Backpropagation (BP) neural network model with measured solar irradiance. The results indicate that the root mean square error (RMSE) of the GRU neural network model is 41% lower than that of the BP neural network model, indicating the improved performance of the proposed model.
AB - In this study, we present a data-driven approach for predicting solar irradiance by applying dimensionality reduction techniques to a dataset comprising of ground meteorological station data and FY-4A remote sensing data collected from January 1st to December 31st, 2018. Specifically, we use Principal Component Analysis (PCA) to reduce the dimensionality of the dataset. Next, we employ a Gated Recurrent Unit (GRU) neural network-based short-term irradiance prediction model to realize the short-term prediction of solar irradiance. We then evaluate the performance of the GRU model by comparing its prediction results with those obtained using a traditional Backpropagation (BP) neural network model with measured solar irradiance. The results indicate that the root mean square error (RMSE) of the GRU neural network model is 41% lower than that of the BP neural network model, indicating the improved performance of the proposed model.
KW - BP neural network
KW - GRU neural network
KW - PCA dimension reduction
KW - solar irradiance prediction
UR - https://www.scopus.com/pages/publications/85164737113
U2 - 10.1109/ICCCBDA56900.2023.10154822
DO - 10.1109/ICCCBDA56900.2023.10154822
M3 - Conference contribution
AN - SCOPUS:85164737113
T3 - 2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023
SP - 605
EP - 609
BT - 2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023
Y2 - 26 April 2023 through 28 April 2023
ER -