Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals.

ABSTRACT: Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed

SUBMITTER: Bamisile O 

PROVIDER: S-EPMC9187635 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature