Titlebook: Data-Driven Modelling of Non-Domestic Buildings Energy Performance; Supporting Building Saleh Seyedzadeh,Farzad Pour Rahimian Book 2021 Th

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書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance被引頻次




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The Child’s and the Practical View of Spacensumption of buildings. These regulations are diverse targeting different areas, new and existing buildings and usage types. This paper reviews the methods employed for building energy performance assessment and summarise the schemes introduced by governments. The challenges with current participate

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Conceptions of Space in Social Thoughtbuilding energy consumption and performance. This chapter provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy

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Conceptions of Space in Social Thoughtfor each ML model and using two simulated building energy data. The use of grid search coupled with cross-validation method in examination of the model parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance o

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978-3-030-64753-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Saleh Seyedzadeh,Farzad Pour RahimianOffers a framework to efficiently select machine learning models to forecast energy loads of buildings.Develops an energy performance prediction model for non-domestic buildings.Provides a case study

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