Optimizing solar irradiance forecasting: ANN models enhanced with ADAM and Cuckoo search algorithm.

ABSTRACT: Renewable energy sources (RES) are being used and integrated into the electrical grid as a result of the environment's effects and the ever-increasing demand for energy. Reliable and accurate forecasts are necessary to address environmental concerns and improve grid management due to the intermittent availability of renewable energy sources. This study focuses on improving ANN-based techniques for precise solar irradiance prediction as the prediction accuracy of an artificial neural network (ANN) is impacted by the random assignment of weights to its edges. As a result, we proposed hybrid solar irradiance forecasting models in which the cuckoo search algorithm (CSA) and adaptive moment estimation (ADAM) are used to optimize the weights assigned to the ANN's edges. Two models were presented

SUBMITTER: Sadiq M 

PROVIDER: S-EPMC12680249 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature