A novel hybrid modelling structure fabricated by using Takagi-Sugeno fuzzy to forecast HVAC systems energy demand in real-time for Basra city. (May 2020)

• Record Type: Journal Article
• Authors: Homod, Raad Z.; Togun, Hussein; Abd, Haider J.; Sahari, Khairul S.M.
• Is Part Of: Sustainable cities and society. Volume 56(2020)
• Publication Date: 2020-05
• Subjects: Nonlinear modeling -- Uncertain disturbance -- Multi-climatic zone model -- Outdoor thermal comfort -- TS Fuzzy identification -- Multi-zone energy forecasting; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102091
• Languages: English
• ISSNs: 2210-6707

Abstract

Graphical abstract: Highlights: Hybrid network structure fabricated by novel TS-FS for forecast HVAC Energy Demand. CMM structure is well suited for storing the parameters and weights of TS-FS. The investigation for the right choice for the zone location saving more than 50 % of HVAC system energy. Thermal comfort zone on the psychrometric chart of ASHRAE Standard is used to select the optimal zone. The input-output data set are systemized into a novel clustering method which to improve forecasting performance. Abstract: 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.