The implementation of long-term forecasting strategies using a knowledge-based expert system: part-II

Abstract

Abstract In this paper, a knowledge-based expert system (ES) is implemented to support the choice of the most suitable load forecasting model, among traditional mathematical techniques of Part I, for medium/long term power system planning. In the proposed ES, the detailed problem statement including forecasting algorithms and the key variables that affect the demand forecasts are firstly identified. So, system planner establishes a multitude of electrical, non-electrical variables for different areas. A set of decision rules relating these variables are then established and stored in the knowledge base. With the knowledge based at hand, a list of realistic models that can reflect accurately the typical system behavior over other models is emulated. Then, the best one is suggested to produce the annual load forecast. A practical application is given to demonstrate the usefulness of the developed prototype.