Developing a precise and strong model for forecasting energy utilization is important aim for the management and functionality of smart buildings. The previous studies have researched different models for forecasting various load prediction schemes. The combined effects regarding data enrichment and machine learning approach in energy predictions have not been fully examined. This research proposes a novel approach, an ensemble model enhanced by generative adversarial networks (GANs) for predicting the usage of energy in big buildings that are commercial. This combined system integrates various single models using ensemble method with stacking. Furthermore, a GAN is utilized to capture the distribution of samples from the main dataset, generating top-notch specimens to augment the dataset from the training data. This expanded dataset allows the model to train with a wider range of samples, increasing its resilience. The experimental series evaluate the method that is proposed, using three variants of GAN and assessing performance with metrics such as mean absolute error, root mean square error, and coefficient of variation of root mean square error. This proposed approach demonstrates practical results that develop a model for power utilization prediction in application of real world.