SM
S. Mohammadi
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1
Natural ventilation has been used widely in buildings to deliver a healthy and comfortable indoor environment for occupants. It also reduces the consumption of energy in the built environment and dilutes the concentration of carbon dioxide. Various methods and techniques have been used to evaluate and predict indoor airspeed and patterns in buildings. However, few studies have been implemented to investigate the relevant methods and tools for the evaluation of ventilation performance in indoor and outdoor spaces. The current study aims to review available methods, identifying reliable ones to apply in future research. This study investigates scientific databases and compares the advantages and drawbacks of methods including analytical models, empirical models, zonal models, and CFD models. The findings indicated the computational fluid dynamics (CFD) model is the most relevant method because of cost-effectiveness, informative technique, and proficiency to predict air velocity patterns and ratios in buildings. Finally, widely used CFD codes and tools are compared considering previous studies. It is concluded the application of codes for research is subject to the complexity and characteristics of a studied model, the area and field of study, the desired turbulence model, and the user interface.
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Natural ventilation has been used widely in buildings to deliver a healthy and comfortable indoor environment for occupants. It also reduces the consumption of energy in the built environment and dilutes the concentration of carbon dioxide. Various methods and techniques have been used to evaluate and predict indoor airspeed and patterns in buildings. However, few studies have been implemented to investigate the relevant methods and tools for the evaluation of ventilation performance in indoor and outdoor spaces. The current study aims to review available methods, identifying reliable ones to apply in future research. This study investigates scientific databases and compares the advantages and drawbacks of methods including analytical models, empirical models, zonal models, and CFD models. The findings indicated the computational fluid dynamics (CFD) model is the most relevant method because of cost-effectiveness, informative technique, and proficiency to predict air velocity patterns and ratios in buildings. Finally, widely used CFD codes and tools are compared considering previous studies. It is concluded the application of codes for research is subject to the complexity and characteristics of a studied model, the area and field of study, the desired turbulence model, and the user interface.
District heating (DH) has a major potential to increase the efficiency, security, and sustainability of energy management at the community scale. However, there is a huge challenge for decision makers due to the lack of knowledge about thermal energy demand during a year. Thermal energy demand is strongly dependent on the outdoor temperature, building area, and activities. In this context, this paper presents an innovative monthly thermal energy mapping method to calculate and visualize heat demand accurately for various types of buildings. The method includes three consecutive phases: (i) calculating energy loss, (ii) completing a dataset that includes energy and building information, and (iii) generating the monthly heat demand maps for the community. Determining the amount of demand and the best location for energy generators from the perspective of energy efficiency in a DH system in an urban context is one of the important applications of heat maps. Exploring heat demand characteristics and visualizing them on maps is the foundation of smart DHs.
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District heating (DH) has a major potential to increase the efficiency, security, and sustainability of energy management at the community scale. However, there is a huge challenge for decision makers due to the lack of knowledge about thermal energy demand during a year. Thermal energy demand is strongly dependent on the outdoor temperature, building area, and activities. In this context, this paper presents an innovative monthly thermal energy mapping method to calculate and visualize heat demand accurately for various types of buildings. The method includes three consecutive phases: (i) calculating energy loss, (ii) completing a dataset that includes energy and building information, and (iii) generating the monthly heat demand maps for the community. Determining the amount of demand and the best location for energy generators from the perspective of energy efficiency in a DH system in an urban context is one of the important applications of heat maps. Exploring heat demand characteristics and visualizing them on maps is the foundation of smart DHs.
Journal article
(2021)
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Saleh Mohammadi, Bauke de Vries, Azarakhsh Rafiee, Masoud Esfandiari, Eduardo Dias
Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively.
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Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively.
Interconnection and generation from a North Sea power hub
A linear electricity model
We research effects on the electricity market of countries surrounding the North Sea after a proposed offshore wind park in the Dogger Bank area of the North Sea has been constructed. Interconnection and generation distribution are analysed separately. The supply price of electricity for each country is calculated by a linear regression analysis to simulate the supply price for higher or lower supply. The model uses the coupling of one supply with one receiver country. Linear modelling of the electricity market combines the results for each objective to find a final state for the market. Using the historic market and weather data for 2016, the results from interconnection show an average generated value of 0.275 [M€/hour] and 82.1 [GW] of average energy flow through the hub. The results of this interconnection between the countries bring between −26% and +11% change on average electricity prices. For hub generation added in, we found an average generated value of 0.573 [M€/hour] and an average price drop of 5% for each country for an average wind power generation of 6.3 [GW] at the hub. The results show that interconnecting the similarly sized electricity markets i.e. Great Britain and Germany & the Netherlands and Denmark, where one has a higher renewable share, would bring the most price stabilization between the two as well as generate the most financial return.
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We research effects on the electricity market of countries surrounding the North Sea after a proposed offshore wind park in the Dogger Bank area of the North Sea has been constructed. Interconnection and generation distribution are analysed separately. The supply price of electricity for each country is calculated by a linear regression analysis to simulate the supply price for higher or lower supply. The model uses the coupling of one supply with one receiver country. Linear modelling of the electricity market combines the results for each objective to find a final state for the market. Using the historic market and weather data for 2016, the results from interconnection show an average generated value of 0.275 [M€/hour] and 82.1 [GW] of average energy flow through the hub. The results of this interconnection between the countries bring between −26% and +11% change on average electricity prices. For hub generation added in, we found an average generated value of 0.573 [M€/hour] and an average price drop of 5% for each country for an average wind power generation of 6.3 [GW] at the hub. The results show that interconnecting the similarly sized electricity markets i.e. Great Britain and Germany & the Netherlands and Denmark, where one has a higher renewable share, would bring the most price stabilization between the two as well as generate the most financial return.
Global warming has become an increasing challenge due to the impact of human activities on the environment. In this regard, university campuses with various activities and departments have a great impact on the environment. Ecological Footprint Analysis (EFA) is a natural resource depletion assessment tool, with a high level of accuracy, that measures the impact of human activities on the environment. Considering the Ecological Footprint (EF) capabilities, this study developed a method to assess the environmental impacts of a university campus using component-based parameters. The goals of the study are to explore the effective components of EF and to propose some policy guidelines to diminish the human impacts on the environment on university campuses. Five components, including natural gas and electricity consumption, water and food usage, and waste production, were measured in a survey from 2013 to 2016 at the building scale. The mean EF of the campus was 16,484 global hectares (gha). Fossil fuel energy had the highest level of environmental impact with 70.73%, followed by waste production and food and water usage with 26.87%, 1.28%, and 1.12%, respectively. The results demonstrate that the EF Index (EFI) of the case study campus was −0.82, which reveals an unsustainable performance. The EF results were illustrated on an Ecological Footprint Map (EFM), which shows the east and west parts of the camps were more unsustainable.
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Global warming has become an increasing challenge due to the impact of human activities on the environment. In this regard, university campuses with various activities and departments have a great impact on the environment. Ecological Footprint Analysis (EFA) is a natural resource depletion assessment tool, with a high level of accuracy, that measures the impact of human activities on the environment. Considering the Ecological Footprint (EF) capabilities, this study developed a method to assess the environmental impacts of a university campus using component-based parameters. The goals of the study are to explore the effective components of EF and to propose some policy guidelines to diminish the human impacts on the environment on university campuses. Five components, including natural gas and electricity consumption, water and food usage, and waste production, were measured in a survey from 2013 to 2016 at the building scale. The mean EF of the campus was 16,484 global hectares (gha). Fossil fuel energy had the highest level of environmental impact with 70.73%, followed by waste production and food and water usage with 26.87%, 1.28%, and 1.12%, respectively. The results demonstrate that the EF Index (EFI) of the case study campus was −0.82, which reveals an unsustainable performance. The EF results were illustrated on an Ecological Footprint Map (EFM), which shows the east and west parts of the camps were more unsustainable.
Journal article
(2021)
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Masoud Esfandiari, Suzaini Mohamed Zaid, Muhammad Azzam Ismail, Mohammad Reza Hafezi, Iman Asadi, S. Mohammadi
Energy consumption to cool an indoor environment is a substantial part of total energy end-use, particularly in a tropical climate with high energy demand for cooling. To improve energy efficiency, cooling systems can be optimized using a variety of neutral indoor temperatures to maintain a balance between an occupant’s thermal comfort and cooling energy demand. This explanatory study investigated the thermal quality and cooling energy demand of a Platinum-certified office building in the tropical climate of Malaysia. The investigation aimed to suggest a balance between occupant thermal comfort and cooling energy demand. The thermal investigation includes an objective field measurement that implements environmental equipment to monitor thermal quality and a subjective occupant’s thermal feedback using a questionnaire survey. To calculate cooling energy demand, the total equivalent temperature difference method (TETD) is applied. The results suggested an occupant’s cooling sensation of around 24 °C, with no significant difference concerning age and gender. Cooling load calculation indicated a 36% energy reduction by increasing air temperature to 26 °C, for occupants to feel thermally comfortable in a tropical climate. These findings contribute to improving sustainable energy policies, sustainable construction, and thermal comfort improvement for a tropical climate.
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Energy consumption to cool an indoor environment is a substantial part of total energy end-use, particularly in a tropical climate with high energy demand for cooling. To improve energy efficiency, cooling systems can be optimized using a variety of neutral indoor temperatures to maintain a balance between an occupant’s thermal comfort and cooling energy demand. This explanatory study investigated the thermal quality and cooling energy demand of a Platinum-certified office building in the tropical climate of Malaysia. The investigation aimed to suggest a balance between occupant thermal comfort and cooling energy demand. The thermal investigation includes an objective field measurement that implements environmental equipment to monitor thermal quality and a subjective occupant’s thermal feedback using a questionnaire survey. To calculate cooling energy demand, the total equivalent temperature difference method (TETD) is applied. The results suggested an occupant’s cooling sensation of around 24 °C, with no significant difference concerning age and gender. Cooling load calculation indicated a 36% energy reduction by increasing air temperature to 26 °C, for occupants to feel thermally comfortable in a tropical climate. These findings contribute to improving sustainable energy policies, sustainable construction, and thermal comfort improvement for a tropical climate.
Journal article
(2021)
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Masoud Esfandiari, Suzaini Mohamed Zaid, Muhammad Azzam Ismail, Mohammad Reza Hafezi, Iman Asadi, S. Mohammadi, Salah Vaisi, Ardalan Aflaki
The quality of the indoor environment has become a vital component for buildings due to the time spent indoors. To this extent, the performance of the indoor environment is considered as part of the greenery criteria by green rating schemes such as the Green Building Index in Malaysia. This study aims to investigate and assess the quality of the indoor environment of Platinum-certified office buildings in a tropical climate. This research applied a case study approach over two Platinum-certified office buildings. Post-occupancy evaluation is employed integrating full-scale measurement with an occupants’ survey. The measurement was carried out from May to August, and 112 questionnaires were retrieved to evaluate occupants’ satisfaction with aspects of the indoor environment. Thermal comfort, indoor air quality, acoustic, lighting, furniture, and cleanliness are considered as the main study variables. The findings of full-scale measurement indicated high relative humidity, and low air velocity and illuminance. While occupants reported overall indoor environment quality (IEQ) comfort, a significant correlation of variables was observed. The main sources of dissatisfaction were identified as overcooling around 24 °C, high relative humidity (RH), around 70% RH, glare, and background noise around 51.9 dB. Statistically, a significant difference between occupants’ responses to IEQ of two cases was identified, although both buildings are labelled with a Platinum certificate.
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The quality of the indoor environment has become a vital component for buildings due to the time spent indoors. To this extent, the performance of the indoor environment is considered as part of the greenery criteria by green rating schemes such as the Green Building Index in Malaysia. This study aims to investigate and assess the quality of the indoor environment of Platinum-certified office buildings in a tropical climate. This research applied a case study approach over two Platinum-certified office buildings. Post-occupancy evaluation is employed integrating full-scale measurement with an occupants’ survey. The measurement was carried out from May to August, and 112 questionnaires were retrieved to evaluate occupants’ satisfaction with aspects of the indoor environment. Thermal comfort, indoor air quality, acoustic, lighting, furniture, and cleanliness are considered as the main study variables. The findings of full-scale measurement indicated high relative humidity, and low air velocity and illuminance. While occupants reported overall indoor environment quality (IEQ) comfort, a significant correlation of variables was observed. The main sources of dissatisfaction were identified as overcooling around 24 °C, high relative humidity (RH), around 70% RH, glare, and background noise around 51.9 dB. Statistically, a significant difference between occupants’ responses to IEQ of two cases was identified, although both buildings are labelled with a Platinum certificate.
This paper describes a step-by-step approach for generating various energy concepts for neighbourhoods, based on local renewable resources. The approach is developed within the European research project ‘Smart Urban Isle’ (SUI). While much literature is focussed on comparison or optimization of predefined configurations, the SUI approach adds to the existing knowledge by introducing a systematic step-by-step approach that supports the first step of the development phase, i.e., the generation of various - potentially innovative - energy system configurations for neighbourhoods, which in the following phase can be optimized using optimization methods. First, the five steps of the approach are introduced, and secondly, these are applied to an existing residential neighbourhood in the Netherlands. The resulting preferred energy concept for the case study consists of a local, ultra-low temperature heat grid, heated by decentralised heat production from PV-thermal (PVT) collectors on individual roofs and connected to a collective seasonal underground storage (ATES). This paper demonstrates the usefulness of the approach for generating various alternative innovative energy concepts for neighbourhoods, based on the local demands and energy potentials, and also describes the resulting energy concept developed for the case study. This innovative energy concept can also be applied to similar residential neighbourhoods.
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This paper describes a step-by-step approach for generating various energy concepts for neighbourhoods, based on local renewable resources. The approach is developed within the European research project ‘Smart Urban Isle’ (SUI). While much literature is focussed on comparison or optimization of predefined configurations, the SUI approach adds to the existing knowledge by introducing a systematic step-by-step approach that supports the first step of the development phase, i.e., the generation of various - potentially innovative - energy system configurations for neighbourhoods, which in the following phase can be optimized using optimization methods. First, the five steps of the approach are introduced, and secondly, these are applied to an existing residential neighbourhood in the Netherlands. The resulting preferred energy concept for the case study consists of a local, ultra-low temperature heat grid, heated by decentralised heat production from PV-thermal (PVT) collectors on individual roofs and connected to a collective seasonal underground storage (ATES). This paper demonstrates the usefulness of the approach for generating various alternative innovative energy concepts for neighbourhoods, based on the local demands and energy potentials, and also describes the resulting energy concept developed for the case study. This innovative energy concept can also be applied to similar residential neighbourhoods.
In 2008, the Chartered Institution of Building Services Engineers (CIBSE TM46 UC) presented an annual-fixed thermal energy benchmark of 240 kWh/m2/yr for university campus (UC) buildings as an attempt to reduce energy consumption in public buildings. However, the CIBSE TM46 UC benchmark fails to consider the difference between energy demand in warm and cold months, as the thermal performance of buildings largely depends on the ambient temperature. This paper presents a new generation of monthly thermal energy benchmarks (MTEBs) using two computational methods including mixed-use model and converter model, which consider the variations of thermal demand throughout a year. MTEBs were generated using five basic variables, including mixed activities in the typical college buildings, university campus revised benchmark (UCrb), typical operation of heating systems, activities impact, and heating degree days. The results showed that MTEBs vary from 24 kWh/m2/yr in January to one and nearly zero kWh/m2/yr in June and July, respectively. Based on the detailed assessments, a typical college building was defined in terms of the percentage of its component activities. Compared with the 100% estimation error of the TM46 UC benchmark, the maximum 21% error of the developed methodologies is a significant achievement. The R-squared value of 99% confirms the reliability of the new generation of benchmarks.
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In 2008, the Chartered Institution of Building Services Engineers (CIBSE TM46 UC) presented an annual-fixed thermal energy benchmark of 240 kWh/m2/yr for university campus (UC) buildings as an attempt to reduce energy consumption in public buildings. However, the CIBSE TM46 UC benchmark fails to consider the difference between energy demand in warm and cold months, as the thermal performance of buildings largely depends on the ambient temperature. This paper presents a new generation of monthly thermal energy benchmarks (MTEBs) using two computational methods including mixed-use model and converter model, which consider the variations of thermal demand throughout a year. MTEBs were generated using five basic variables, including mixed activities in the typical college buildings, university campus revised benchmark (UCrb), typical operation of heating systems, activities impact, and heating degree days. The results showed that MTEBs vary from 24 kWh/m2/yr in January to one and nearly zero kWh/m2/yr in June and July, respectively. Based on the detailed assessments, a typical college building was defined in terms of the percentage of its component activities. Compared with the 100% estimation error of the TM46 UC benchmark, the maximum 21% error of the developed methodologies is a significant achievement. The R-squared value of 99% confirms the reliability of the new generation of benchmarks.