Abstract
This paper presents an implemented architectural framework for the 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, and knowledge-based and case-based reasoning. IFCS is also a hybrid programming tool, which allows the developer to implement forecasters by means of object-oriented visual programming, knowledge-based programming and procedural programming. The system was implemented on the real-time expert-system shell G2, with the G2 Diagnostic Assistant (GDA) and NeurOn-Line (NOL) modules. Rules, procedures and flow diagrams are organized into a hierarchy of workspaces. The modularity of IFCS allows the subsequent addition of other modules of intelligent techniques. IFCS was applied for daily power-load prediction in the city of Regina. The power-load data set was separated into subclasses, and a neural-network module consisting of backpropagation networks was applied to each of them. The data set was also modeled using a linear regression (LR) and a case-based reasoning (CBR) program, and their results were compared to those from the neural-network approach.
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