Abstract
This study proposes hybrid solar radiation temporal modeling approaches to support the design of clean energy systems using deep learning techniques and statistical distribution fitting. Solar radiation data are analyzed using a probability distribution to determine whether they follow a known statistical pattern, focusing on total solar radiation on a tilted surface (MJ/m(2)) ([Formula: see text]). Maximum likelihood estimation (MLE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) are used to optimize the process of estimating probability distribution parameters. Subsequently, the cumulative distribution function (CDF) is constructed, and a particular distribution profile is applied to replace the inherent randomness in [Formula: see text] data during the preparation phase of estimation model inputs. In the next step, innovative hybrid [Formula: see text] temporal modeling approaches based on CDF are developed using long short-term memory networks (LSTMs), gated recurrent units (GRUs), and extreme gradient boosting (XGBoost) algorithms. Model results are evaluated through Jensen-Shannon divergence (JSD) analysis. Thus, the DOD-Boost framework is established. According to the findings from comprehensive analyses, DOD-Boost models that integrated a modeling approach for [Formula: see text], optimization techniques, data preprocessing strategies, and temporal modeling achieved highly accurate predictions. Among all tested models, the Weibull (WOA) - LSTM - XGBoost model achieved the best distributional accuracy, with the lowest JSD value of 0.0084. The JSD metric was prioritized as it provides a more comprehensive assessment of performance by measuring the similarity of the predicted and actual data distributions, which is more informative than simple point predictions for energy planning. Consequently, this study provides a transferable hybrid model for PV-based energy planning that can also be used in developing countries.