Hybrid Evolutionary Optimization and Neural Network Models for Climate Adaptive Renewable Energy Forecasting

Abstract

The paper gives a Hybrid Evolutionary Optimization and Neural Network Model of climate adaptive renewable energy forecasting. The need to increase renewable energy has led to the creation of more precise and flexible forecasting tools. This paper integrates evolutionary optimization techniques, including Genetic algorithms (GA) and Particle Swarm optimization (PSO), with deep learning neural networks, specifically Long short-term memory (LSTM), to predict renewable energy production using solar and wind energy. The methodology will consist of gathering up-to-date environmental measurements (temperature, wind speed, and solar radiation), as well as the previous data on energy generation by using renewable resources. The hyperparameters of the deep learning model are optimized using evo0.lutionary optimization algorithms to make accurate predictions of the model under different climatic conditions. The proposed hybrid model was compared to the conventional models, such as ARIMA and SVM, and the outcomes indicate that it has a better performance with respect to the accuracy of the prediction, the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), and the R2 value. The hybrid model also saves a lot of time when it comes to predicting failure, thus it is more effective in proactive energy management. Also, the model can be adjusted to changing conditions of the environment and offers real-time predictions and useful information concerning the optimization of energy production and its integration into the power grid. The results indicate that this mixed method has the potential to maximize the accuracy and effectiveness of renewable energy prediction, which will further result in improved energy grid management and low operation costs.