kumoh national institute of technology
Networked Systems Lab.

Mohtasin Golam, Rubina Akter, Jae Min Lee, and Dong Seong Kim, "LSTM Based Solar Irradiance Prediction from Remote Sensing of Meteorological Data", IEEE Geoscience and Remote Sensing Letters, January 2021. (Resubmitted)
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Date : 2021-01-10
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Solar irradiance prediction is an indispensable area to the photovoltaic (PV) power management system. However, PV management may be subject to serious penalties due to the unsteadiness pattern of photovoltaic output power that depends on solar radiation. To overcome this problem, a high-precision LSTM based neural network model named SIPNet to predict solar irradiance in a short time interval is proposed. Solar radiation depends on the environmental sensing of meteorological information such as, temperature, pressure, humidity, wind speed and direction which are different dimensions in measurement. LSTM neural network can concurrently learn the spatiotemporal of multivariate input features via various logistic gates. Moreover, SIPNet can estimate the future solar irradiance given the historical observation of the meteorological information and the radiation data. The SIPNet model is simulated and compared with the actual and predicted data series and evaluated by the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE). The empirical results show that the value of MAE, MSE and RMSE of SIPNet is 0.0413, 0.0033 and 0.057 respectively, which demonstrate the effectiveness of SIPNet and outperforms other existing models.