Renewable Energy Forecasting using Deep Learning Techniques

Abstract

A detailed research on deep learning in renewable energy forecasting shows how sophisticated algorithms may improve prediction accuracy. The research explores deep learning models and finds intriguing aspects that improve predictions. Long Short-Term Memory (LSTM) networks can capture temporal relationships in energy data, making them successful in predicting short-term variations with a prediction accuracy boost of 18.18% over ARIMA. Convolutional Neural Networks (CNNs) capture spatial correlations in huge datasets with up to 13% accuracy. With its capacity to analyze sequential data, Recurrent Neural Networks (RNNs) can capture long-term patterns and improve forecasting accuracy by 29.41% over Support Vector Machines. In addition, LSTM’s better handling of non-linear connections in wind energy data has improved prediction accuracy by 14.29% over feedforward networks. These results demonstrate how deep learning approaches improve renewable energy forecasting with unparalleled precision and dependability. As shown in diverse applications, LSTM, CNN, and RNN models improve renewable energy forecasting efficiency and efficacy, boosting sustainable energy solution innovation.

Keywords

• renewable energy
• renewable energy
• deep learning
• photovoltaics
• energy storage