Hamed Yassaghi is an experienced Energy Engineer that works towards saving energy and promoting energy efficiency within buildings. Hamed received his Doctor of Philosophy - PhD Degree in Architectural Engineering from Drexel University and has a background in Mechanical Engineering and Energy Engineering. Hamed is passionate about enhancing a sustainable built environment and works towards developing strategies to adapting to, and mitigating climate change and has contributed to many scientific publications in this regard.
Buildings are subject to many uncertainties ranging from thermophysical performance to user activ... more Buildings are subject to many uncertainties ranging from thermophysical performance to user activity. Climate change is an additional source of uncertainty that complicates building performance evaluation. This study aims to quantify the share of uncertainties stemming from building factors, user behavior, and climate uncertainty from boilers, chillers, fans, pumps, total HVAC systems, and total site energy use. A novel method combining Monte Carlo analysis and ANOVA is proposed to partition uncertainties from building energy simulation results under different climate change scenarios. The Monte Carlo method is used to generate distributions of building and user factors as building simulation inputs. Then, simulation results under current and future climate conditions are post-processed using a three-way ANOVA technique to discretize the uncertainties for a reference office building in Philadelphia, PA. The proposed method shows the share in percentages of each input factor (buildin...
It is becoming increasingly crucial to develop methods and strategies to assess building performa... more It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.
Buildings are subject to many uncertainties ranging from thermophysical performance to user activ... more Buildings are subject to many uncertainties ranging from thermophysical performance to user activity. Climate change is an additional source of uncertainty that complicates building performance evaluation. This study aims to quantify the share of uncertainties stemming from building factors, user behavior, and climate uncertainty from boilers, chillers, fans, pumps, total HVAC systems, and total site energy use. A novel method combining Monte Carlo analysis and ANOVA is proposed to partition uncertainties from building energy simulation results under different climate change scenarios. The Monte Carlo method is used to generate distributions of building and user factors as building simulation inputs. Then, simulation results under current and future climate conditions are post-processed using a three-way ANOVA technique to discretize the uncertainties for a reference office building in Philadelphia, PA. The proposed method shows the share in percentages of each input factor (building, user, and climate) in the total uncertainty of building energy simulation output results. Our results indicate that the contribution of climate uncertainty increases from current conditions to future climate scenarios for chillers, boilers, fans, and pumps’ electricity use. User parameters are the dominant uncertainty factor for total site energy use and fans’ electricity use. Moreover, boiler and HVAC energy use are highly sensitive to the shape and range of user and building input factor distributions. We underline the importance of selecting the appropriate distribution for input factors when partitioning the uncertainties of building performance modeling
It is becoming increasingly crucial to develop methods and strategies to assess building performa... more It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.
Buildings are subject to significant stresses due to climate change and design strategies for cli... more Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and h...
The authors regret to report that the paper “Reverse QMRA as a Decision Support Tool: Setting Acc... more The authors regret to report that the paper “Reverse QMRA as a Decision Support Tool: Setting Acceptable Concentration Limits for Pseudomonas aeruginosa and Naegleria fowleri” contains some erroneous computations [...]
Opportunistic premise plumbing pathogens such as Pseudomonas aeruginosa and Naegleria fowleri are... more Opportunistic premise plumbing pathogens such as Pseudomonas aeruginosa and Naegleria fowleri are a growing concern in building water systems because of their potential risks to human health. The aim of this study was to determine the critical concentrations of P. aeruginosa and N. fowleri in water that are associated with meaningful public health risks. To determine these concentrations, a reverse quantitative microbial risk assessment (QMRA) was conducted. Environmental concentrations of P. aeruginosa and N. fowleri corresponding to the risk target of one micro-disability-adjusted life year (DALY) per person per year and 10−4 annual risks of illness were calculated for several applicable exposure scenarios. To calculate the concentration of P. aeruginosa, cleaning contact lenses with potentially contaminated tap water in the absence of an appropriate cleaning solution was considered. For N. fowleri, two exposure scenarios, recreational exposure (swimming) and nasal cleansing (via ...
Buildings are subject to many uncertainties ranging from thermophysical performance to user activ... more Buildings are subject to many uncertainties ranging from thermophysical performance to user activity. Climate change is an additional source of uncertainty that complicates building performance evaluation. This study aims to quantify the share of uncertainties stemming from building factors, user behavior, and climate uncertainty from boilers, chillers, fans, pumps, total HVAC systems, and total site energy use. A novel method combining Monte Carlo analysis and ANOVA is proposed to partition uncertainties from building energy simulation results under different climate change scenarios. The Monte Carlo method is used to generate distributions of building and user factors as building simulation inputs. Then, simulation results under current and future climate conditions are post-processed using a three-way ANOVA technique to discretize the uncertainties for a reference office building in Philadelphia, PA. The proposed method shows the share in percentages of each input factor (buildin...
It is becoming increasingly crucial to develop methods and strategies to assess building performa... more It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.
Buildings are subject to many uncertainties ranging from thermophysical performance to user activ... more Buildings are subject to many uncertainties ranging from thermophysical performance to user activity. Climate change is an additional source of uncertainty that complicates building performance evaluation. This study aims to quantify the share of uncertainties stemming from building factors, user behavior, and climate uncertainty from boilers, chillers, fans, pumps, total HVAC systems, and total site energy use. A novel method combining Monte Carlo analysis and ANOVA is proposed to partition uncertainties from building energy simulation results under different climate change scenarios. The Monte Carlo method is used to generate distributions of building and user factors as building simulation inputs. Then, simulation results under current and future climate conditions are post-processed using a three-way ANOVA technique to discretize the uncertainties for a reference office building in Philadelphia, PA. The proposed method shows the share in percentages of each input factor (building, user, and climate) in the total uncertainty of building energy simulation output results. Our results indicate that the contribution of climate uncertainty increases from current conditions to future climate scenarios for chillers, boilers, fans, and pumps’ electricity use. User parameters are the dominant uncertainty factor for total site energy use and fans’ electricity use. Moreover, boiler and HVAC energy use are highly sensitive to the shape and range of user and building input factor distributions. We underline the importance of selecting the appropriate distribution for input factors when partitioning the uncertainties of building performance modeling
It is becoming increasingly crucial to develop methods and strategies to assess building performa... more It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.
Buildings are subject to significant stresses due to climate change and design strategies for cli... more Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and h...
The authors regret to report that the paper “Reverse QMRA as a Decision Support Tool: Setting Acc... more The authors regret to report that the paper “Reverse QMRA as a Decision Support Tool: Setting Acceptable Concentration Limits for Pseudomonas aeruginosa and Naegleria fowleri” contains some erroneous computations [...]
Opportunistic premise plumbing pathogens such as Pseudomonas aeruginosa and Naegleria fowleri are... more Opportunistic premise plumbing pathogens such as Pseudomonas aeruginosa and Naegleria fowleri are a growing concern in building water systems because of their potential risks to human health. The aim of this study was to determine the critical concentrations of P. aeruginosa and N. fowleri in water that are associated with meaningful public health risks. To determine these concentrations, a reverse quantitative microbial risk assessment (QMRA) was conducted. Environmental concentrations of P. aeruginosa and N. fowleri corresponding to the risk target of one micro-disability-adjusted life year (DALY) per person per year and 10−4 annual risks of illness were calculated for several applicable exposure scenarios. To calculate the concentration of P. aeruginosa, cleaning contact lenses with potentially contaminated tap water in the absence of an appropriate cleaning solution was considered. For N. fowleri, two exposure scenarios, recreational exposure (swimming) and nasal cleansing (via ...
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