TY - GEN
T1 - Preliminary Analysis of Short-term Solar Irradiance Forecasting by using Total-sky Imager and Convolutional Neural Network
AU - Ryu, Anto
AU - Ito, Masakazu
AU - Ishii, Hideo
AU - Hayashi, Yasuhiro
N1 - Funding Information:
The authors would like to acknowledge the Leading Graduate Program in Science and Engineering, Waseda University from MEXT, Japan. This work was also supported by JST CREST Grant Number JPMJCR15K5, Japan
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/15
Y1 - 2019/5/15
N2 - The installation of photovoltaic system (PV) is increasing rapidly across the world. However, the fluctuation of PV output causes serious challenges in the power grid operation. Among the fluctuation, a quick part of fluctuation is mainly caused by the change of cloud coverage. A total-sky imager (TSI), measuring device to take sky and cloud, could be useful for short-term solar irradiance forecasting. In this paper, a convolutional neural network (CNN) is applied to forecasting model (called CNN model) to forecast 5-20 min ahead of global horizontal irradiance (GHI) using total-sky images and lagged GHI. To verify the effectiveness of CNN, three forecasting models are compared. They are the persistence model, the CNN model using only total-sky images, and the CNN model using both total-sky images and lagged GHI. From the computation, the proposed CNN model using both total-sky images and lagged GHI performs root-mean-square error (RMSE) of 49-177W/m2, 93-146W/m2, 71-118W/m2 in sunny day, partly cloudy day and overcast day, respectively. From these results, the proposed method is shown to be suitable for short-term solar irradiance forecasting.
AB - The installation of photovoltaic system (PV) is increasing rapidly across the world. However, the fluctuation of PV output causes serious challenges in the power grid operation. Among the fluctuation, a quick part of fluctuation is mainly caused by the change of cloud coverage. A total-sky imager (TSI), measuring device to take sky and cloud, could be useful for short-term solar irradiance forecasting. In this paper, a convolutional neural network (CNN) is applied to forecasting model (called CNN model) to forecast 5-20 min ahead of global horizontal irradiance (GHI) using total-sky images and lagged GHI. To verify the effectiveness of CNN, three forecasting models are compared. They are the persistence model, the CNN model using only total-sky images, and the CNN model using both total-sky images and lagged GHI. From the computation, the proposed CNN model using both total-sky images and lagged GHI performs root-mean-square error (RMSE) of 49-177W/m2, 93-146W/m2, 71-118W/m2 in sunny day, partly cloudy day and overcast day, respectively. From these results, the proposed method is shown to be suitable for short-term solar irradiance forecasting.
KW - Convolutional neural network
KW - Solar irradiance forecasting
KW - short-term forecasting
KW - total-sky imager
UR - http://www.scopus.com/inward/record.url?scp=85067038761&partnerID=8YFLogxK
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U2 - 10.1109/GTDAsia.2019.8715984
DO - 10.1109/GTDAsia.2019.8715984
M3 - Conference contribution
AN - SCOPUS:85067038761
T3 - 2019 IEEE PES GTD Grand International Conference and Exposition Asia, GTD Asia 2019
SP - 627
EP - 631
BT - 2019 IEEE PES GTD Grand International Conference and Exposition Asia, GTD Asia 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE PES GTD Grand International Conference and Exposition Asia, GTD Asia 2019
Y2 - 19 March 2019 through 23 March 2019
ER -
