Solar radiation forecasting using gradient boosting based ensemble learning model for various climatic zones

Abstract

India is seeing a massive boost in solar power installations in the recent years. It is expected that solar energy utilization will be increasing exponentially in near future. Solar radiation forecasting is an essential tool to obtain the optimal performance out of the solar systems. An ensemble model using gradient boosting has been developed for hourly global horizontal irradiance forecasting is proposed for the various climatic zones of India. The gradient boost-based model is benchmarked with Auto Regressive Integrated Moving Average (ARIMA),2-layer feed forward neural network, and Long Short-Term Memory (LSTM). Gradient boosting obtained with marginally better performance metrics, mean absolute error (20.97 W/m2) and mean square error (1357 W2/m4) as compared to that of the neural network (21.66 W/m2, 1479.53 W2/m4) and performed much better than ARIMA and LSTM in terms of mean absolute error (251.66 W/m2) & mean square error (82,290 W2/m4) and mean absolute error (67.60 W/m2) & mean square error (8123 W2/m4). The diverse climatic zones in India and feature importance analysis have been considered during the model development and testing phase. The proposed model is also intended to have practical implications in obtaining solar irradiance data in locations with minimal availability of solar radiation data and solar power systems due to the model ability to provide good performance metrics in a smaller timeframe making it an ideal candidate for real-time solar power predicting model.