Deep Learning-Based Auto-LSTM Approach for Renewable Energy Forecasting: A Hybrid Network Model

Abstract

In recent years, renewable energy forecasting has gained increasing attention due to its potential to minimize energy resource usage and maximize the security of power plant operation. Deep learning models have emerged as a promising tool for renewable energy prediction. However, the application of these techniques for renewable energy forecasting remains sparse. In this work, we introduce a deep belief network-based auto-LSTM approach that utilizes a wireless sensor network (WSN) for energy forecasting in solar power plants. We also compare this approach with other deep learning techniques, including long short-term memory (LSTM) and artificial neural networks, as well as traditional models such as Multilayer Perception and physical prediction models. We evaluate the performance of these methods using data from 26 solar plants. Our experimental analysis shows that the proposed auto-LSTM-based deep learning approach outperforms the other models in terms of prediction accuracy, demonstrating its efficiency and effectiveness for renewable energy forecasting in solar power plants. By introducing the inventive auto-LSTM model and assessing its effectiveness in comparison to diverse established techniques, our study not only adds to the current discussions on predictive modeling but also tackles the tangible challenges linked with the integration of renewable energy.