Green building was one of the solutions proposed by professionals to cope with the carbon emission problem by evaluating building energy performance. Energy modelling of buildings evaluates the energy use of building through simulating and accounting the energy related components. The microclimate environment which is one of the inputs to energy simulation is difficult to accurately predict and needs another simulation while the drawback of simulation is it takes time and knowledge to build and run the simulation model.
This research aims to develop prediction models that sufficient to become alternative of the microclimate simulation using machine learning and artificial intelligence based model. This study uses Typical Meteorological Year (TMY) data to perform street blocks and microclimate analysis using computational fluid dynamics (ENVI-MET 4.0) software to analyze 20 street block clusters in Taipei.
These analyses can be used to lead a design team towards integrated aesthetic and functional design decision that considers the complexities of the street blocks and the effect of microclimate on urban-level wind flow. Estimation program that are made in MATLAB and R.GUI platform verified and suitable to cope with the dataset and created the best estimation model using the propagation algorithm. Visualization-aided tools in form of GUI also developed to complement the estimation models created.

Green building was one of the solutions proposed by professionals to cope with the carbon emission problem by evaluating building energy performance. Energy modelling of buildings evaluates the energy use of building through simulating and accounting the energy related components. The microclimate environment which is one of the inputs to energy simulation is difficult to accurately predict and needs another simulation while the drawback of simulation is it takes time and knowledge to build and run the simulation model.
This research aims to develop prediction models that sufficient to become alternative of the microclimate simulation using machine learning and artificial intelligence based model. This study uses Typical Meteorological Year (TMY) data to perform street blocks and microclimate analysis using computational fluid dynamics (ENVI-MET 4.0) software to analyze 20 street block clusters in Taipei.
These analyses can be used to lead a design team towards integrated aesthetic and functional design decision that considers the complexities of the street blocks and the effect of microclimate on urban-level wind flow. Estimation program that are made in MATLAB and R.GUI platform verified and suitable to cope with the dataset and created the best estimation model using the propagation algorithm. Visualization-aided tools in form of GUI also developed to complement the estimation models created.

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