commotion
发表于 2025-3-21 16:52:09
书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0263304<br><br> <br><br>书目名称Data-Driven Modelling of Non-Domestic Buildings Energy Performance读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0263304<br><br> <br><br>
消音器
发表于 2025-3-21 20:15:04
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
albuminuria
发表于 2025-3-22 00:30:22
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
为敌
发表于 2025-3-22 08:12:39
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
甜瓜
发表于 2025-3-22 10:41:12
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Amenable
发表于 2025-3-22 15:49:15
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Amenable
发表于 2025-3-22 19:43:27
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割让
发表于 2025-3-22 21:18:45
978-3-030-64753-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
的染料
发表于 2025-3-23 02:42:54
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
browbeat
发表于 2025-3-23 09:35:35
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