The ongoing growth of the world's population has led to a dramatic increase in renewable energy consumption as nations transition toward cleaner energy systems; therefore, accurately forecasting renewable energy consumption becomes vital for efficient energy management and sustainable economic development. This article introduces a comparative hybrid forecasting framework for forecasting renewable energy consumption. Ensemble machine learning (ML), Simple Exponential Smoothing (SES), and the Holt-Winters model constitute the hybrid framework. The Explainable Artificial Intelligence (XAI) technique explains the best ensemble predictive model's outcome. Forecast evaluations on a hold-out test set and validation across a 24-month future forecast period are presented, along with graphs and evaluation metrics comparing the developed models. This research focuses on the efficiency of integrating an ensemble learning model with a classical time series method for forecasting renewable energy consumption with a unique set of features. And provides a groundwork for future advancement.