Novel residual hybrid machine learning for solar activity prediction in smart cities

Predicting global solar activity is crucial for smart cities, especially for space activities, communication industries, and climate change monitoring. The recently developed models to predict solar activity based on stand-alone artificial intelligence, using machine and deep learning models, and hybrid models are promising. Yet they may not be effective at capturing simpler linear patterns in the data and often fail to provide reliable predictions due to their computational cost and complexity. This article proposed a novel residual hybrid machine learning method integrating linear regression machine learning, and deep learning neural networks for solving predictive accuracy in individual machine learning models that reduces complexity. The residual hybrid model leverages the capacities of the support vector machine (SVM) and long short-term memory neural network (LSTM) for hybrid SVM-LSTM model. The performance of the model is evaluated using the correlation coefficient, determination coefficient, root-mean-squared error (RMSE) and mean-absolute error. The simulation results indicated that compared to the SVM-LSTM, the training and testing RMSE of the LSTM is reduced by 76.62% and 71.18%, respectively. It also decreases the training and testing RMSE of the SVM by 77.06% and 71.81%, respectively. The proposed model can be implemented as reliable solution for accurately predicting solar activities in smart cities.