Long-term load forecasting for fast developing utility using a knowledge-based expert system

Abstract

The application of the classical forecasting methods, when applied to a fast developing utility with a period characterized by fast and dynamic changes, are insufficient and may provide an invaluable dimension to the decision making process. In this paper, a knowledge-based expert system (ES) is implemented to support the choice of the most suitable load forecasting model for medium/long term power system planning. In the proposed ES, the detailed problem statement including forecasting algorithms and the key variables (electrical and nonelectrical variables) that affect the demand forecasts are firstly identified. A set of decision rules relating these variables are then obtained and stored in the knowledge base. Afterwards, the best model that will reflect accurately the typical system behavior over other models is suggested to produce the annual load forecast. A practical application is given to demonstrate the usefulness of the developed prototype system.