A toolset for construction of hybrid intelligent forecasting systems: application for water demand prediction

Abstract

This paper presents the Intelligent Forecasters Construction Set (IFCS) which is a toolset for constructing forecasting applications. The toolset supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning. The developer can construct a forecasting application using rules, procedures and flow diagrams, which are organized into a hierarchy of workspaces. The modularity of the IFCS allows subsequent addition of other modules of intelligent techniques. The IFCS was used for developing a water demand forecasting system based on real-world data obtained from the City of Regina's water distribution system and Environment Canada. A utility demand prediction system developed with the IFCS system is useful for optimizing operation costs of water plants. Some water plants need to pay a flat rate for electricity, which is set depending on peak kilowatt demand. Hence, if the peak kilowatt demand can be reduced, the operating costs of the plant can be lessened (Jamieson RA et al. American Water Works Association Journal 1993;85:48–55). An energy management system needs a good estimate of future customer demand in order to find the optimal pumping schedules that can minimize the peak kilowatt demand. Since the IFCS supports developing multiple predictor models, modeling of data can be expedited. The benefits of using multiple modules of artificial neural networks for demand prediction are presented. The results from this approach are compared with a linear regression and a case-based reasoning program. The performance comparisons among the forecasters will be discussed.