A flexible load forecasting model for integrated resources planning

Abstract

This paper presents an innovative and flexible load forecasting model suitable to support formulation of integrated resources plans. The envisaged load forecasting model is built upon state-space data representation sequentially updated by Kalman filtering algorithm. It allows dynamic insertion of new data and inclusion of qualitative yardsticks in the energy planning process. Successful implementation of the model on case study shows that the proposed approach is flexible for insertion of external data to the forecasting model.