Simulation study of power grid investment demand forecasting model based on gray clustering algorithm

The simulation of demand forecasting models is a critical component in determining investment needs for the power grid. However, traditional approaches often suffer from inaccuracies in identifying actual demand. In particular, conventional genetic algorithms fail to effectively address the challenges of predictive modeling in this context, leading to unsatisfactory outcomes. To tackle this issue, this study introduces a novel simulation approach for forecasting power grid investment demand, utilizing a gray clustering algorithm. The research begins by applying gray system theory to identify and classify key influencing factors, thereby minimizing the impact of irrelevant variables on the forecasting process. Based on this foundation, a simulation framework is constructed using the gray clustering method. Comprehensive analysis is then conducted on the model's simulation outcomes. Results from MATLAB simulations indicate that, according to specific evaluation metrics, the proposed gray clustering algorithm outperforms traditional genetic algorithms, offering higher accuracy and more efficient factor processing in power grid demand forecasting.