The Experts below are selected from a list of 34386 Experts worldwide ranked by ideXlab platform
Xi Chen - One of the best experts on this subject based on the ideXlab platform.
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a multi stage optimization of passively designed high rise residential Buildings in multiple Building operation scenarios
Applied Energy, 2017Co-Authors: Xi Chen, Hongxing YangAbstract:This article proposes a two-stage design optimization approach which is applied to a prototype passively designed high-rise residential Building under different ventilation modes and thermal load requirements. Machine learning methods are employed to develop surrogate models for improving the computation efficiency of the multi-objective optimization process. The surrogate model is trained by modeling experiments with EnergyPlus and R, which can provide reliable energy performance indicators of generic Building models featuring passive design parameters including the Building Layout, envelope thermophysics, Building geometry and infiltration & air-tightness. The lighting, cooling and heating demands of the generic Building model are determined by the hybrid ventilation and light diming control strategies in compliance with local green Building assessment criteria in Hong Kong. The multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and support vector machines (SVM) are examined by the statistical modelling. SVM is capable of fitting a surrogate model with the best prediction performance based on the coefficient of determination and root mean square error. In addition, both single-sided ventilation and cross-ventilation models under varied thermal load requirements are investigated to compare the preferable design solutions for each scenario. The multi-stage optimization approach is also applied to a Mediterranean climate to explore optimal design solutions in more diverse external environmental conditions. This research can provide a highly efficient design optimization tool to appropriately deploy passive architectural strategies in a green Building project.
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Developing a new passive design approach for green Building assessment scheme with integrated statistical analysis and optimization
The Hong Kong Polytechnic University, 2017Co-Authors: Xi ChenAbstract:xxxviii, 272 pages : color illustrationsPolyU Library Call No.: [THS] LG51 .H577P BSE 2017 ChenThis thesis aims to develop a novel passive design assessment system for green Building rating tools with an example application to the Building environmental assessment method (BEAM) in Hong Kong. Current passive design approach is criticized for arguable criteria allocation, unjustified weighting system and incompatibility with the traditional whole Building energy simulation method. Therefore, a modelling based statistical analysis and optimization framework is proposed to improve the integration of passive designs for green Building assessment in this thesis, which can address the synergy of energy and indoor environmental requirements under both free-running and artificially controlled conditions. This holistic design approach can account for the interactive effect between different passive strategies and enable decision-makers to understand the relative importance of each strategy and deploy them appropriately at the first opportunity for achieving the optimum Building performance in future. The developed new passive design evaluation system can achieve consistent rating with the traditional assessment approach as proved by a successful application in a green Building case study. The Building orientation, external obstruction angle, external wall thermal resistance, wall specific heat, window transmittance, window U-value, window to ground ratio, overhang projection ratio and infiltration air mass flowrate coefficient were determined as representative input design factors highly-concerned in early design stages based on a comprehensive literature review of passive strategies related to the Building Layout, envelop thermophysics, Building geometry and infiltration & air-tightness. After determining input variables and their distribution functions, a generic Building model was developed and on-site measurement methods were specified to obtain the ventilation, daylight, thermal comfort and corresponding energy use indicators. The influence of selected passive design parameters on multiple indoor environmental indicators was initially quantified by standard regression analysis. After the verification of independence between input parameters, control variables including the running periods, internal loads, ventilation control methods and weather conditions were modulated to observe their impacts on both the coefficient of determination and sensitivity indices. Two regression model indices were found to vary greatly with different control variable settings when the humidity ratio, PMVSET and TSENS were taken as Building performance outputs for the cooling period. Despite the observed variation, the window transmittance and geometry features were constantly the most influential design factors over other indoor environmental indices. In addition, the ASHRAE adaptive model with 90% acceptability was proved to be the most suitable comfort assessment model for assessing naturally ventilated Buildings during the whole cooling seasonin hot and humid climates.On top of standard regression analysis, the required minimum sampling size, effectiveness of rank transformations, uncertainty of sensitivity indices and prediction accuracy of different meta-models were evaluated by the statistical modeling and comparative analysis. A large sample size over 100 per regression coefficient was recommended to obtain stable statistical estimations of the prediction error and goodness of fit. The rank transformation managed to calibrate sensitivity coefficients by up to 6%, which even altered the rankings between certain design inputs. Uncertainties of sensitivity indices were validated to be within 2% with adequate bootstrap repetitions. Prediction accuracy of the ventilation and comfort model were also improved to an acceptable level with the non-parametric regression analysis. Succeeding to above statistical analyses, on-site measurements and simulations were conducted on an existing Public Rental Housing (PRH) development to verify the compliance with green Building requirements and explore the performance improving potential with different ventilation strategies. An unoccupied naturally ventilated flat was found to fulfill the acceptability limits in the ASHRAE adaptive model for 67.5% of the time in July, while the required minimum ventilation rate of 2.0 ACH was achieved. More than 95.6% of the habitable area, subject to different obstruction levels and window to floor ratios, complied with the related daylight assessment criteria. In addition, full-day ventilation proved to be the best control strategy for low thermal mass Buildings, while the simulated indoor conditions were partly verified by on-site measurement results. The energy saving potential with combined natural ventilation and daylight strategies was also anticipated to be 51.9% for air-conditioning and 8.3% for lighting compared to a baseline Building. Based on the above modelling and experimental studies, a holistic design optimization process was proposed by integrating robust sensitivity analysis into multi-objective optimizations. Global sensitivity indices and corresponding significance statistics were first calculated to prune the optimization problem space. The NSGA-II based optimization was then conducted to obtain the Pareto frontier, which presented a trade-off between comfort and daylight objectives when the minimum ventilation rate was fulfilled. After a further post optimization analysis and comparison of decision-making methods, the final optimum solution for the passive design achieved a 11.2% reduction of the total unmet time. In addition, different population sizes, crossover probabilities and mutation rates were examined to find the most suitable setting of NSGA-II by balancing between the computation efficiency and optimization productivity. This simulation-based optimization process was further applied to four other cities in hot and humid areas. Finally, a new passive design assessment system as an equivalent alternative to the traditional whole Building energy simulation was established with the application of the holistic design approach. Extensive global sensitivity analysis with regression, variance-based and screening-based methods were performed on the prototype Building to constantly readjust the criteria coverage based on feedbacks from statistical and optimization analysis. With the five design variables finalized for the new assessment framework, upper and lower performance scales were derived from baseline simulations and NSGA-II based optimizations. The grading coefficient was further determined by a local sensitivity analysis for a pro-rata credit awarding scheme. Weighting systems transformed from different sensitivity indices were validated by modelling experiments, where FAST first-order indices outstood by accurately predicting 73.3% of test cases. The new system has also been successfully applied to a registered green Building project for obtaining consistence with the traditional approach. Such a systematic approach detailed in this study can also be exploited to develop alternative approachesfor all performance-based criteria ina green Building rating scheme.Department of Building Services EngineeringPh.D., Department of Building Services Engineering, The Hong Kong Polytechnic University, 2017Doctorat
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Developing a meta-model for sensitivity analyses and prediction of Building performance for passively designed high-rise residential Buildings
Applied Energy, 2017Co-Authors: Xi Chen, Hongxing Yang, Ke SunAbstract:This paper aims to develop a green Building meta-model for a representative passively designed high-rise residential Building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic Building model and provide reliable Building performance predictions. Input parameters are selected from different passive design strategies including the Building Layout, envelop thermophysics, Building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green Building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust Building performance predictions associated with the green Building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.
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a comprehensive sensitivity study of major passive design parameters for the public rental housing development in hong kong
Energy, 2015Co-Authors: Xi Chen, Weilong ZhangAbstract:This paper presents a comprehensive SA (sensitivity analysis) of the typical PRH (public rental housing) development in Hong Kong based on a combined Building energy, daylight and AFN (airflow network) simulation. A generic Building model is constructed with a proposed MM (mixed-mode) ventilation control strategy to fulfill the thermal comfort requirement in the local green Building guidance. The numerical modeling results are used to conduct both local and global sensitivity analyses to determine the relative importance of major passive design parameters, which comprehensively cover design aspects of the Building Layout, envelop thermophysics, Building geometry and infiltration & air-tightness. The calculated global and local sensitivity indices on the cooling energy prove that the window solar heat gain coefficient, window to ground ratio, external obstruction and overhang projection fraction are the four most influential passive design factors. Similar results are also obtained when the lighting energy is specified as the output of the sensitivity analysis. The optimized Building model derived from the sensitivity analysis is anticipated to achieve an energy saving of 41.6% compared to the baseline model as stipulated by the local Building regulation. It is believed that sensitivity analysis is useful for identifying crucial design parameters to facilitate further optimization of the Building performance in early architectural design stages.
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a comprehensive review on passive design approaches in green Building rating tools
Renewable & Sustainable Energy Reviews, 2015Co-Authors: Xi Chen, Hongxing Yang, Lin LuAbstract:Buildings are the major consumers of energy in Hong Kong and most urban areas in the world. Building environmental assessment schemes, and green Building rating tools (GBRTs) have been adopted by architects, engineers and researchers for more than 20 years to help promote more sustainable construction activities. Each rating tool highlights energy use as a significant portion of the assessment and provides guidance on more energy efficient strategies. Building energy efficiency can usually be improved by both passive and active technologies. Active design involves making more energy efficient heating, ventilation, air-conditioning (HVAC) systems, hot water production, lighting and any other Building services application, whereas passive design focuses more on Building envelope related aspects determined by the architectural design so as to reduce the demand of the Building for energy. Recently, there has been renewed interest in passive strategies because of the low extra investment and the potential benefits in energy saving. The passive design approach has also been recognized in the latest versions of green Building rating tools. Five representative rating systems, which all developed their own passive design criteria leading to the award of credits, are subject to comparative examinations in respect of the comprehensiveness, effectiveness and accuracy of each criterion in this paper. Passive design criteria including the Building Layout, envelope thermophysics, Building geometry, air-tightness and infiltration performance and their effects on Building energy consumption are also comprehensively reviewed. The results show that a holistic design approach based on passive energy saving strategies proves to be an effective way to reduce Building energy budgets. However, more consolidated weighting systems to enable comparison of different passive strategies should be incorporated in green Building rating tools based on further sensitivity and parametric studies.
Hongxing Yang - One of the best experts on this subject based on the ideXlab platform.
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a multi stage optimization of passively designed high rise residential Buildings in multiple Building operation scenarios
Applied Energy, 2017Co-Authors: Xi Chen, Hongxing YangAbstract:This article proposes a two-stage design optimization approach which is applied to a prototype passively designed high-rise residential Building under different ventilation modes and thermal load requirements. Machine learning methods are employed to develop surrogate models for improving the computation efficiency of the multi-objective optimization process. The surrogate model is trained by modeling experiments with EnergyPlus and R, which can provide reliable energy performance indicators of generic Building models featuring passive design parameters including the Building Layout, envelope thermophysics, Building geometry and infiltration & air-tightness. The lighting, cooling and heating demands of the generic Building model are determined by the hybrid ventilation and light diming control strategies in compliance with local green Building assessment criteria in Hong Kong. The multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and support vector machines (SVM) are examined by the statistical modelling. SVM is capable of fitting a surrogate model with the best prediction performance based on the coefficient of determination and root mean square error. In addition, both single-sided ventilation and cross-ventilation models under varied thermal load requirements are investigated to compare the preferable design solutions for each scenario. The multi-stage optimization approach is also applied to a Mediterranean climate to explore optimal design solutions in more diverse external environmental conditions. This research can provide a highly efficient design optimization tool to appropriately deploy passive architectural strategies in a green Building project.
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Developing a meta-model for sensitivity analyses and prediction of Building performance for passively designed high-rise residential Buildings
Applied Energy, 2017Co-Authors: Xi Chen, Hongxing Yang, Ke SunAbstract:This paper aims to develop a green Building meta-model for a representative passively designed high-rise residential Building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic Building model and provide reliable Building performance predictions. Input parameters are selected from different passive design strategies including the Building Layout, envelop thermophysics, Building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green Building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust Building performance predictions associated with the green Building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.
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a comprehensive review on passive design approaches in green Building rating tools
Renewable & Sustainable Energy Reviews, 2015Co-Authors: Xi Chen, Hongxing Yang, Lin LuAbstract:Buildings are the major consumers of energy in Hong Kong and most urban areas in the world. Building environmental assessment schemes, and green Building rating tools (GBRTs) have been adopted by architects, engineers and researchers for more than 20 years to help promote more sustainable construction activities. Each rating tool highlights energy use as a significant portion of the assessment and provides guidance on more energy efficient strategies. Building energy efficiency can usually be improved by both passive and active technologies. Active design involves making more energy efficient heating, ventilation, air-conditioning (HVAC) systems, hot water production, lighting and any other Building services application, whereas passive design focuses more on Building envelope related aspects determined by the architectural design so as to reduce the demand of the Building for energy. Recently, there has been renewed interest in passive strategies because of the low extra investment and the potential benefits in energy saving. The passive design approach has also been recognized in the latest versions of green Building rating tools. Five representative rating systems, which all developed their own passive design criteria leading to the award of credits, are subject to comparative examinations in respect of the comprehensiveness, effectiveness and accuracy of each criterion in this paper. Passive design criteria including the Building Layout, envelope thermophysics, Building geometry, air-tightness and infiltration performance and their effects on Building energy consumption are also comprehensively reviewed. The results show that a holistic design approach based on passive energy saving strategies proves to be an effective way to reduce Building energy budgets. However, more consolidated weighting systems to enable comparison of different passive strategies should be incorporated in green Building rating tools based on further sensitivity and parametric studies.
Feng Yang - One of the best experts on this subject based on the ideXlab platform.
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urban design to lower summertime outdoor temperatures an empirical study on high rise housing in shanghai
Building and Environment, 2011Co-Authors: Feng Yang, Stephen S Y Lau, Feng QianAbstract:Abstract This research investigates the effect of urban design factors on summertime urban heat island (UHI) intensity. Ten high-rise residential quarters in the inner city of Shanghai were empirically investigated during mid July to mid August in 2008. On-site design variables were developed to quantify the thermal impacts from density, Building Layout and greenery. The design variables that were measured on site were correlated with the variation in UHI intensity during the day and night. The results show that variations in UHI are in part due to site planning, Building design, and greenery. The overall daytime and nighttime UHI models explain up to 77 and 90 percent of UHI variation, respectively. On-site shading from either Buildings or vegetation canopy is the most important factor influencing daytime UHI. The shading factor can distort and dilute behavior of other variables, e.g., green ratio and surface albedo. Nighttime UHI is more complicated due to the influence from anthropogenic heat, and is significantly related to greenery density and coverage. Based on the findings, potential design strategies are proposed in an effort to mitigate UHI, including manipulating Building Layout and mass to improve shading during the day while facilitating site ventilation at night and increasing site vegetation cover through strategic tree planting. Further recommendations for urban planning approaches to mitigate UHI on the urban scale are proposed.
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summertime heat island intensities in three high rise housing quarters in inner city shanghai china Building Layout density and greenery
Building and Environment, 2010Co-Authors: Feng Yang, Stephen Siu Yu Lau, Feng QiaAbstract:Shanghai as the largest city in China has been suffering from the ever-worsening thermal environment due to the explosive urbanization rate. As an indication of urbanization impact, urban heat islands (UHI) can give rise to a variety of problems. This paper reports the results of an empirical study on the summertime UHI patterns in three high-rise residential quarters in the inner-city Shanghai. Site-means of UHI intensity are compared; case studies are carried out on strategically located measurement points; and regression analysis is followed to examine the significance of the on-site design variables in relation to UHI intensity. It is found that site characteristics in plot Layout, density and greenery have different impacts on UHI-day and UHI-night patterns. Day-time UHI is closely related to site shading factor. Total site factor (TSF) as an integrated measure on solar admittance shows a higher explanatory power in UHI-day than sky view factor (SVF) does under a partially cloudy sky condition. Night-time UHI cannot be statistically well explained by the on-site variables in use, indicating influences from anthropogenic heat and other sources. Evaporative cooling by vegetation plays a more important role at night than it does at day. Considered diurnally, the semi-enclosed plot Layout with a fairly high density and tree cover has the best outdoor thermal condition. Design implication based on the findings, with consideration on other important environmental design issues, is briefly discussed.
Ke Sun - One of the best experts on this subject based on the ideXlab platform.
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Developing a meta-model for sensitivity analyses and prediction of Building performance for passively designed high-rise residential Buildings
Applied Energy, 2017Co-Authors: Xi Chen, Hongxing Yang, Ke SunAbstract:This paper aims to develop a green Building meta-model for a representative passively designed high-rise residential Building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic Building model and provide reliable Building performance predictions. Input parameters are selected from different passive design strategies including the Building Layout, envelop thermophysics, Building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green Building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust Building performance predictions associated with the green Building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.
Feng Qian - One of the best experts on this subject based on the ideXlab platform.
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urban design to lower summertime outdoor temperatures an empirical study on high rise housing in shanghai
Building and Environment, 2011Co-Authors: Feng Yang, Stephen S Y Lau, Feng QianAbstract:Abstract This research investigates the effect of urban design factors on summertime urban heat island (UHI) intensity. Ten high-rise residential quarters in the inner city of Shanghai were empirically investigated during mid July to mid August in 2008. On-site design variables were developed to quantify the thermal impacts from density, Building Layout and greenery. The design variables that were measured on site were correlated with the variation in UHI intensity during the day and night. The results show that variations in UHI are in part due to site planning, Building design, and greenery. The overall daytime and nighttime UHI models explain up to 77 and 90 percent of UHI variation, respectively. On-site shading from either Buildings or vegetation canopy is the most important factor influencing daytime UHI. The shading factor can distort and dilute behavior of other variables, e.g., green ratio and surface albedo. Nighttime UHI is more complicated due to the influence from anthropogenic heat, and is significantly related to greenery density and coverage. Based on the findings, potential design strategies are proposed in an effort to mitigate UHI, including manipulating Building Layout and mass to improve shading during the day while facilitating site ventilation at night and increasing site vegetation cover through strategic tree planting. Further recommendations for urban planning approaches to mitigate UHI on the urban scale are proposed.
