PV ENERGY FORECASTING USING DEEP LEARNING ALGORITHM

Abstract

Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SVM for accurate solar power forecasting with higher relibaility. In this paper, a GA-CNN deep learning algorithm is employed with an optimized hyperparameters technique for PV energy forecasting. This technique outperforms when compared with the other methods such as LSTM, KNN-SVM, and CNN-RNN techniques in terms of RMSE, MAE, MSE and R-Square performance indices. This method provides a better and more robust method of deep learning for solar PV energy forecasting.