標(biāo)題: Titlebook: Data-Driven Modelling of Non-Domestic Buildings Energy Performance; Supporting Building Saleh Seyedzadeh,Farzad Pour Rahimian Book 2021 Th [打印本頁] 作者: commotion 時間: 2025-3-21 16:52
書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance影響因子(影響力)
書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance影響因子(影響力)學(xué)科排名
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書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance被引頻次
書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance被引頻次學(xué)科排名
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書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance年度引用學(xué)科排名
書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance讀者反饋
書目名稱Data-Driven Modelling of Non-Domestic Buildings Energy Performance讀者反饋學(xué)科排名
作者: 消音器 時間: 2025-3-21 20:15
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作者: albuminuria 時間: 2025-3-22 00:30
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作者: 為敵 時間: 2025-3-22 08:12
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作者: 甜瓜 時間: 2025-3-22 10:41 作者: Amenable 時間: 2025-3-22 15:49 作者: Amenable 時間: 2025-3-22 19:43 作者: 割讓 時間: 2025-3-22 21:18
978-3-030-64753-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 的染料 時間: 2025-3-23 02:42
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 作者: browbeat 時間: 2025-3-23 09:35 作者: etidronate 時間: 2025-3-23 09:59
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% [.].作者: PHON 時間: 2025-3-23 16:21
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.作者: SNEER 時間: 2025-3-23 19:36
Data-Driven Modelling of Non-Domestic Buildings Energy Performance978-3-030-64751-3Series ISSN 1865-3529 Series E-ISSN 1865-3537 作者: Pigeon 時間: 2025-3-23 22:56 作者: VOC 時間: 2025-3-24 03:42
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.作者: 魅力 時間: 2025-3-24 08:07
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.作者: Seminar 時間: 2025-3-24 11:02 作者: watertight, 時間: 2025-3-24 16:16
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.作者: 意外的成功 時間: 2025-3-24 20:48 作者: 調(diào)色板 時間: 2025-3-24 23:26 作者: 用樹皮 時間: 2025-3-25 03:29
Conceptions of Space in Social Thoughtl parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated.作者: antecedence 時間: 2025-3-25 11:29 作者: CHOP 時間: 2025-3-25 14:22
Building Energy Data-Driven Model Improved by Multi-objective Optimisation,sed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies.作者: 小平面 時間: 2025-3-25 18:23 作者: sorbitol 時間: 2025-3-25 20:55 作者: Handedness 時間: 2025-3-26 01:14 作者: Compass 時間: 2025-3-26 06:12 作者: 粗俗人 時間: 2025-3-26 11:52
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% [.].作者: 金絲雀 時間: 2025-3-26 12:41 作者: Frisky 時間: 2025-3-26 19:37
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作者: 減去 時間: 2025-3-26 21:21 作者: 針葉樹 時間: 2025-3-27 01:20 作者: Substance 時間: 2025-3-27 08:29 作者: Urgency 時間: 2025-3-27 13:22 作者: Graves’-disease 時間: 2025-3-27 16:00
Data-Driven Modelling of Non-Domestic Buildings Energy PerformanceSupporting Building 作者: 易怒 時間: 2025-3-27 19:43
Book 2021ing energy performances...This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings..作者: 吹氣 時間: 2025-3-27 23:48 作者: Thyroiditis 時間: 2025-3-28 05:36 作者: phlegm 時間: 2025-3-28 07:36 作者: conscience 時間: 2025-3-28 12:41
ympiad-level problems. We try to provide some of that background and experience by point- out useful theorems and techniques and by providing a suitable ing collection of examples and exercises. This book covers only a fraction of the topics normally rep- resented in competitions such as the USAMO a作者: modest 時間: 2025-3-28 16:16 作者: Wernickes-area 時間: 2025-3-28 19:31
2730-7549 es with the opposing “Mechanists” on the issue of emergence are still worth studying and largely ignored in the many recent works on this subject. Taken as a whole, the book is a goldmine of insights into both the foundations of physics and Soviet history..978-3-030-70047-8978-3-030-70045-4Series ISSN 2730-7549 Series E-ISSN 2730-7557 作者: 挫敗 時間: 2025-3-29 01:29 作者: 無動于衷 時間: 2025-3-29 04:22
,S(q) als kollineationsgruppe des 3-dimensionalen projektiven Raumes über GF(q),
