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

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dc.citedby31
dc.contributor.authorHomod R.Z.en_US
dc.contributor.authorTogun H.en_US
dc.contributor.authorAbd H.J.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorid36994633500en_US
dc.contributor.authorid36638687200en_US
dc.contributor.authorid55548856600en_US
dc.contributor.authorid57218170038en_US
dc.date.accessioned2023-05-29T08:10:17Z
dc.date.available2023-05-29T08:10:17Z
dc.date.issued2020
dc.descriptionClimate control; Cooling systems; Differential equations; Energy management; Energy utilization; Forecasting; Fuzzy neural networks; HVAC; Nonlinear equations; Nonlinear systems; Real time systems; Uncertainty analysis; Climatic zone; Energy forecasting; Non-linear model; Outdoor thermal comfort; T-S fuzzy identifications; Uncertain disturbances; Fuzzy inferenceen_US
dc.description.abstractthe 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. � 2020 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102091
dc.identifier.doi10.1016/j.scs.2020.102091
dc.identifier.scopus2-s2.0-85079685656
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079685656&doi=10.1016%2fj.scs.2020.102091&partnerID=40&md5=c5244915228ac5529583a04861187656
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25506
dc.identifier.volume56
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleSustainable Cities and Society
dc.titleA novel hybrid modelling structure fabricated by using Takagi-Sugeno fuzzy to forecast HVAC systems energy demand in real-time for Basra cityen_US
dc.typeArticleen_US
dspace.entity.typePublication
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