the HVAC systems consume more than half of the total buildings energy demand, forecasting the cooling/heating load of the building is important to predict buildings energy demand. The energy assessment tools such as a model for forecasting building energy consumption is based on outdoor thermal conditions, the outdoor conditions are highly nonlinear in real life cannot be represented by linear differential equations and have an uncertain disturbance nature. This paper contrives a novel nonlinear model structure to cope with such difficulty, which is composed of two hybrid nonlinear forms, Takagi-Sugeno fuzzy system (TS-FS) and Neural Networks’ Weights. Such a structure has many advantages, including suitability for multi-layer implementations like an integrated eight-dimension net of parameters and weights which represents model input-output relations of a nonlinear system. The Gauss-Newton algorithm is used to tune model weights and parameters for the fitting of nonlinear regression of clusters model to data. The main feature of the proposed model is to express the dynamic conditions of the outdoor thermal environment of each fuzzy implication by a cluster functions model and thus promote the prediction performance. The overall proposed model is tested on the training and validation of multizone then compared with the RLF model. The corresponding results show that a better hybrid modelling and uncertainty mitigation which is achieved without significant loss of prediction accuracy.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Civil and Structural Engineering
- Renewable Energy, Sustainability and the Environment