A methodology for modeling HVAC components using evolving fuzzy rules

Abstract

A methodology for the evolutionary construction of fuzzy rule-based (FRB) models is proposed in the paper. The resulting models are transparent and existing expert knowledge could easily be incorporated into the model. An additional advantage of the model is represented by the economy in computational effort in generating the model output. A new encoding mechanism is used that allows the fuzzy model rule base structure and parameters to be estimated from training data without establishing the complete rule list. It uses rule indices and therefore significantly reduces the computational load. The rules are extracted from the data without using a priori information about the inherent model structure. It makes FRB models as flexible as other types of 'black-box' models (neural networks, polynomial models etc.) and in the same time significantly more transparent, especially when only small subset of all possible rules is considered. This approach is applied to modelling of components of heating ventilating and air-conditioning (HVAC) systems. The FRB models have potential applications in simulation, control and fault detection and diagnosis.