Predicting solar power potential via an enhanced ANN through the evolution of cub to predator (ECP) optimization technique

Abstract

Forecasting plays a vital role in solar power generation and skillfully managing renewable energy resources. The traditional artificial neural network (ANN) has certain limitations with its pre-defined network structure in predicting solar power potential and not suitable because traditional ANN must be configured. Configuring the ANN model is time-consuming through manual process or trial and error. Therefore, this work evaluates the combination of different optimization techniques for ANN model configuration. In other techniques, the forecast performance is between 84.9 and 91.6% on average. Experience in handling the traditional methods helps develop a novel evolution of cub to predator (ECP) optimization technique. The investigation revealed that incorporating optimization results in superior performance over the traditional ANN, whereas the proposed ECP unveils 97.2%, which is an excellent result of contest optimization techniques. At the same time, the proposed model with the lowest MSE = 0.0029, and MRE = 0.0809. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.