Multi-factor forecasting of enterprise power load based on STIRPAT and ARMA models

This study developed a corporate electricity load forecasting model that integrates the STIRPAT and ARMA models, assessing the impact of various economic, technological, and policy factors on load. The use of time series forecasting improved the accuracy of medium- and long-term predictions. Empirical results indicate that the model effectively captures dynamic changes in load, providing a scientific basis for corporate electricity planning. Future research may incorporate mor e advanced machine learning methods to further enhance the model's predictive power and applicability.