Leveraging Weather Parameters in Generative Adversarial Networks for Energy Consumption Prediction

energy forecasting is essential for optimizing resource allocation, improving grid management, and achieving energy efficiency in diverse applications. Lately, Generative Adversarial Networks (GANs) have proven to be a potent instrument in this area. This manuscript presents an overview of the application of GANs to energy forecasting, specifically focusing on their utilization in modeling and predicting energy consumption patterns including weather parameters Incorporating weather data into energy forecasting models has been a crucial development, as weather conditions significantly impact energy demand and supply. This manuscript examines the performance of Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) models in predicting energy consumption, highlighting their capacity to capture complex dependencies between weather parameters and energy usage. Using a dataset encompassing energy consumption data and weather parameters over 4.5 months, the study trains and evaluates these models, findings show that GANs outperform CNNs and LSTMs in MSE, MAE, and RMSE measures. This superiority underscores the potential of GANs for accurate energy forecasting. Despite these promising results, the study acknowledges limitations like dataset size constraints and computational complexities.