Enhanced solar power prediction using CNN and ladybug beetle optimization algorithm

Abstract

Integrating renewable energy sources like solar power into the grid necessitates accurate prediction methods to optimize their utilization. This paper proposes a novel approach that combines Convolutional Neural Networks (CNN) with the Ladybug Beetle Optimization (LBO) algorithm to forecast solar power generation efficiently. Many traditional models, for predicting power often struggle with accuracy and efficiency when it comes to computations. To overcome these challenges, we utilize the capabilities of CNN to extract features and recognize patterns from past irradiance data. The CNN structure is skilled at capturing relationships within the input data allowing it to detect patterns that are natural in solar irradiance changes. Additionally, we apply the LBO algorithm inspired by how ladybug beetles search for food to tune the parameters of the CNN model. LBO imitates how ladybug beetles explore to find solutions making it effective in adjusting the hyperparameters of the CNN. This research utilizes a dataset with solar irradiance readings to train and test the proposed CNN-LBO framework. The performance of this model is assessed using evaluation measures, like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), MAPE, and R2 value. The experimental outcomes indicate that our hybrid CNN-LBO method surpasses existing techniques in terms of efficiency.