An implemented framework for the construction of hybrid intelligent forecasting systems

This thesis presents an implemented architectural framework for construction of hybrid intelligent forecasters for utility demand prediction. The framework has been implemented as the Intelligent Forecasters Construction Set (IFCS) which supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning. This tool provides a rapid application development (RAD) environment for constructing forecasting applications. IFCS is also a hybrid-programming tool, which allows developers to implement forecasters by means of object-oriented visual programming, knowledge-based programming and procedural programming. IFCS was implemented on the real-time expert system shell G2{dollar}sp1{dollar} with G2 Diagnostic Assistant (GDA{dollar}sp1{dollar}) and NeurOn-Line{dollar}sp1{dollar} (NOL) modules. Rules, procedures and flow diagrams are organized into a hierarchy of workspaces. The modularity of IFCS allows subsequent addition of other modules of intelligent techniques. A chief benefit of IFCS is that it allows developers to concentrate on problem solving and conceptual modeling instead of dealing with complicated programming tasks. It also expedites implementation of forecasters.; The framework and the IFCS tool were tested on two problem domains. The first application is to predict daily power load of the City of Regina. The second application is to forecast consumer demand on the water distribution system of the City of Regina. The data of each problem was separated into several classes, then a neural network module was applied to model each of them. The results from this approach were compared to those from a linear regression (LR) and a case based reasoning (CBR) program. The forecasting results and performance comparisons among the forecasters will be discussed. ftn {dollar}sp1{dollar} G2, GDA and NeurOn-Line are trademarks of Gensym Corp., U.S.A.