A Hybrid Approach for the Ensembles of Neural Networks for Solar Power Forecasting

Abstract

In this study, a unique hybrid method to solar power forecasting is presented. This strategy makes use of ensemble approaches, which include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs). A more accurate prediction of solar power production may be achieved using the hybrid ensemble, which takes advantage of the specific characteristics of each neural network design. Extensive trials conducted using datasets taken from the actual world demonstrate that the suggested technique is superior to both individual neural network models and conventional methods of predicting. Therefore, the ensemble is a feasible option for practical solar power forecasting applications, helping to the effective utilisation of renewable energy resources. Its flexibility to changing weather conditions, interpretability, and superior accuracy position it as a promising solution.