Enhancing prosumer microgrid sustainability and cost efficiency through quantum conditional generative adversarial network based load and weather forecasting

In smart grids, Prosumer Microgrids (PMGs) play a crucial role as active consumers that both generate and sell electricity. However, managing PMGs efficiently is challenging due to uncertainties in electricity demand and weather conditions like wind speed, ambient temperature, and solar irradiance. These characteristics are difficult to forecast using traditional optimization techniques, which raises operating expenses. This study presents a hybrid technique for accurate load demand and weather data forecasting in order to overcome these challenges. The major objective of the proposed method is to reduce the operating cost of PMG. The hybrid approach is the combination of Quantum Conditional Generative Adversarial Network (QCGAN) and Binary Battle Royale Optimizer Algorithm (BBROA) and hence it is named QCGAN-BBROA. The QCGAN approach is used for the prediction of load demand and weather data using historical data and the BBROA is used to optimize the operating cost of prosumer microgrid. The MATLAB platform is employed to exclude the proposed strategy, and metrics like the root mean square error (RMSE), mean squared error (MSE), and coefficient of determination (R2), were used to assess its performance. These results were compared against existing techniques, including Teaching-Learning-Based Optimization (TLBO), Proximal Policy Optimization (PPO), and Monte Carlo Tree Search Algorithm (MCTSA). Findings show that the proposed approach significantly reduces operational costs, achieving a cost value of 1.162$, which is lower than competing methods. This research contributes to the field by enhancing PMG efficiency through advanced AI-driven forecasting and optimization, offering a novel and practical solution for smart grid applications.