Dual Reservoir-based Deep Learning Architecture for High-Resolution Solar Irradiance Prediction
TL;DR
Although the proposed dual-reservoir architecture consistently achieved the best overall forecasting performance, the improvement over the Single Reservoir–CNN–LSTM variant was not statistically significant at [Formula: see text], indicating that the second reservoir provides a moderate but consistent enhancement rather than a statistically dominant gain.
Abstract
Accurate short-term solar irradiance forecasting is a critical enabler for Photovoltaic (PV) power integration, and energy management in renewable-dominated power systems. Nevertheless, solar irradiance exhibits strong nonlinearity, and temporal variability, which significantly limit the performance of conventional models. To address these challenges among others, in this study we propose a Dual Reservoir-based Convolutional Neural Network Integrated with Long Short-Term Memory (DR–CNN–LSTM) architecture for high-resolution solar irradiance prediction. The proposed framework introduces two parallel reservoir modules to enrich nonlinear state representations and enhance short- and mid-term temporal memory. The proposed model is validated using multi-year solar irradiance data from multiple stations of the California Irrigation Management Information System (CIMIS) and the Solar Radiation Research Laboratory (SRRL) dataset. Extensive experimentation was conducted across multiple forecasting horizons and benchmarked against state-of-the-art models, including LSTM, CNN–LSTM, CNN–DeepESN and single-reservoir hybrid architectures. Forecasting performance is evaluated using MAE, RMSE, MAPE, and the coefficient of determination ([Formula: see text]). Compared with the CNN–LSTM baseline, the proposed framework achieved average RMSE reductions of 10.7% on CIMIS and 11.5% on SRRL, while MAE was reduced by 14.0% and 15.1%, respectively. Furthermore, compared with the state-of-the-art CNN–DeepESN model, the proposed model achieved RMSE reductions of approximately 5.2% on CIMIS and 5.5% on SRRL. Statistical analyses using Friedman, Nemenyi, and Wilcoxon signed-rank tests confirmed significant improvements over most baseline models. Although the proposed dual-reservoir architecture consistently achieved the best overall forecasting performance, the improvement over the Single Reservoir–CNN–LSTM variant was not statistically significant at [Formula: see text], indicating that the second reservoir provides a moderate but consistent enhancement rather than a statistically dominant gain.