Global solar energy estimation using improved greedy based genetic algorithm with deep convolutional neural network

Abstract

Demand for solar energy increases and it is required to manage the supply of energy effectively. Accurate detection on patterns of energy consumed assist in taking appropriate decisions on generating energy. Even though many traditional techniques have predicted the consumption rate, still improvement is needed in prediction accuracy. The pre-processing is performed initially for handling missing values. The feature selection is accomplished using improved greedy based genetic algorithm (GGA) to extract best features to enhance the performance of the model. Output from feature-selection is passed as input to the classification phase using proposed deep convolutional neural network (CNN) in which future solar energy patterns are classified and predicted timely basis power consumption and it optimize the model by minimize the error. The prediction accuracy is estimated through evaluation metrics such as mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) 0.423, 0.652, and 0.215, respectively. The outcomes achieved in terms of accuracy at 99.75, precision at 99.28, sensitivity, and recall at 100 revealed the efficiency of the proposed classification model. As a result, the proposed future prediction of solar energy was considered efficient since it achieved reduced error values than other prediction algorithms. It assists in maintaining stability in solar-energy based systems.