A residual ensemble learning approach for solar irradiance forecasting.

Solar irradiance forecasting plays an essential role in efficient solar energy systems and managing power demand sustainably. In present work, a new residual ensemble learning approach, which consists of two advanced base models, namely Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs), is proposed for solar irradiance forecasting. A model performance depends on data utilized for modeling and the modeling approach employed on the data. This paper focuses on both these aspects of the forecast model by proposing a three module approach. Firstly, a mechanism is proposed for the collection and analysis of multiple-site data surrounding the target location. A hexagon gridding system based algorithm is proposed for selection of multiple sites neighboring the target location. Then, correlation and feature importance scores are utilized as measures for feature selection to choose the most relevant data for forecasting target solar irradiance. In the second module, a residual ensemble learning model is proposed to forecast solar irradiance. The proposed framework is inspired by the hybrid forecast mechanism that considers the linear and non-linear characteristics for modeling. Advanced DNN models of Recurrent Neural Networks are also exploited for developing an accurate and robust model. The last module performs the integration of the deep neural network information and predicts the future values of solar irradiance. For a reliable and comprehensive assessment, the proposed framework is validated with data from four different solar power sites obtained from NASA's POWER repository. The residual ensemble model is trained on past 36 years of data as input for forecasting one day ahead, four days ahead and ten days ahead values of solar irradiance. Performance evaluation is carried out by comparing the prediction results with other models, including benchmark persistence, deep neural networks, and recurrent neural network approaches on performance indexes of MSE and RMSE. The proposed model shows an improvement in forecast performance by approximately 2.5 percent in prediction error. The predictive performance and stability make the proposed residual ensemble learning approach a reliable solar irradiance prediction model. [ABSTRACT FROM AUTHOR]