Photovoltaic (PV) power is subject to variability, influenced by factors such as meteorological conditions. This variability introduces uncertainties in forecasting, underscoring the necessity for enhanced forecasting models to support the large-scale integration of PV systems. Moreover, the presence of missing data during the model development process significantly impairs model performance. To address this, it is essential to impute missing data from the collected datasets before advancing with model development. Recent advances in imputation methods, including Multivariate Imputation by Chained Equations (MICEs), K-Nearest Neighbors (KNNs), and Generative Adversarial Imputation Networks (GAINs), have exhibited commendable efficacy. Nonetheless, models derived solely from a single imputation method often exhibit diminished performance under varying weather conditions. Consequently, this study introduces a weighted average ensemble model that combines multiple imputation-based models. This innovative approach adjusts the weights according to “sky status” and evaluates the performance of single-imputation models using criteria such as sky status, root mean square error (RMSE), and mean absolute error (MAE), integrating them into a comprehensive weighted ensemble model. This model demonstrates improved RMSE values, ranging from 74.805 to 74.973, which corresponds to performance enhancements of 3.293–3.799% for KNN and 3.190–4.782% for MICE, thereby affirming its effectiveness in scenarios characterized by missing data.