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

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楼主: commotion
发表于 2025-3-23 09:59:06 | 显示全部楼层
Introduction,gly, the enhancement of energy efficiency of buildings has become an essential matter in order to reduce the amount of gas emission as well as fossil fuel consumption. An annual saving of 60 billion Euro is estimated as a result of the improvement of EU buildings energy performance by 20% [.].
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Machine Learning for Building Energy Forecasting,building 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 performance.
发表于 2025-3-23 19:36:24 | 显示全部楼层
Data-Driven Modelling of Non-Domestic Buildings Energy Performance978-3-030-64751-3Series ISSN 1865-3529 Series E-ISSN 1865-3537
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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 performance.
发表于 2025-3-24 08:07:55 | 显示全部楼层
https://doi.org/10.1007/978-1-349-16433-2This chapter, first, reviews evaluation indices for the efficient retrofit plan to enhance building energy performance, second, provides the concept and mathematical demonstration of multi-objective optimisation (MOO) and finally presents the potential of using MOO for supporting the development of retrofitting strategies.
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Multi-objective Optimisation and Building Retrofit Planning,This chapter, first, reviews evaluation indices for the efficient retrofit plan to enhance building energy performance, second, provides the concept and mathematical demonstration of multi-objective optimisation (MOO) and finally presents the potential of using MOO for supporting the development of retrofitting strategies.
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