Fisher information and online SVR-based dynamic modeling methodology for meteorological sensitive load forecasting in smart grids

A novel dynamic modeling methodology for meteorological sensitive load forecasting of smart grids is proposed by using Fisher information theory and online support vector regression (OSVR) technology to improve load prediction accuracy in this paper. According to the changes in the operating state of the smart grids and streaming data characteristics, the OSVR model is employed to implement accurate online training algorithms to avoid retraining the entire training data whenever a sample is added to or removed from the training set. On the other hand, the paper originally utilizes Fisher Information theory to address the introduction of weather factors into the meteorological sensitive load prediction model and feature selection for it. We also present a practical and concise implementation of the proposed methodology. We demonstrate the application of the proposed methodology in the meteorological sensitive load prediction for the local utilities in different periods and compared it with the traditional method. The results indicate that the forecast model constructed by the proposed methodology can obtain the superior prediction performance among the conventional SVR models.