Performance Comparison of LSTM and ESN Models in Time-Series Prediction of Solar Power Generation

Improving the prediction accuracy of solar power generation is a critical challenge in promoting sustainable energy solutions. While machine learning models like long short-term memory (LSTM) have gained attention, they face practical limitations such as their complex structure, long training time, and susceptibility to overfitting. Echo state networks (ESNs) have attracted attention for their small number of trainable parameters and fast training speed, but their sensitivity to hyperparameter settings makes performance improvement difficult. In this study, the key hyperparameters of an ESN (spectral radius, input noise, and leakage rate) were optimized to maximize performance. The ESN achieved a Root Mean Square Error (RMSE) of 0.0069 for power prediction, demonstrating a significant improvement in accuracy over a tuned LSTM model. ESNs are also well-suited for real-time prediction and large-scale data processing, owing to their low computational cost and fast training speed. By providing a more accurate and efficient forecasting tool, this study supports grid operators in managing the intermittency of renewable energy, thereby fostering a more stable and reliable sustainable energy infrastructure.