Building height is a key geometric attribute for generating 3-D building models. We propose a nov... more Building height is a key geometric attribute for generating 3-D building models. We propose a novel four-stage approach for automated estimation of building heights from their shadows in very high resolution (VHR) multispectral images. First, a building's actual shadow regions are detected by applying ratio-band algorithm to the VHR image. Second, 2-D building footprint geometries are identified using graph theory and morphological fuzzy processing techniques. Third, artificial shadow regions are simulated using the identified building footprint and solar information in the image metadata at predefined height increments. Finally, the difference between the actual and simulated shadow regions at every height increment is computed using Jaccard similarity coefficient. The estimated building height corresponds to the height of the simulated shadow region that resulted in the maximum value for Jaccard index. The algorithm is tested on seven urban sites in Cardiff, U.K. with various levels of morphological complexity. Our method outperforms the past attempts, and the mean error is reduced by at least 21%.
There is a growing interest among healthcare managers and designers in moving towards a 'patient-... more There is a growing interest among healthcare managers and designers in moving towards a 'patient-centred' design of health and care facilities by integrating patient perceptions and expectations of the physical environment where care takes place. Increased interests in physical environments can mostly be attributed to our improved understanding of their role in patients' health outcomes and staff productivity. There is a gap in the literature on users' perspectives on physical settings in the context of healthcare. Moreover, the connection of care services with the design of the facility is often overlooked partly due to the lack of evidence. This research was aimed at filling the gap by exploring outpatients' perspectives on design factors related to the areas frequented by them, e.g., hospital waiting areas. A 16-item questionnaire was conducted among randomly selected outpatients in two hospitals in Qingdao, China, with a response rate of 84.3%. Five principal factors were identified: sensory; lighting and thermal; facilities; spatial; and seating design, which agreed with the literature. Non-parametric tests were applied to assess variances in constructed principal dimensions concerning demographic variables. Female outpatients were found to be more perceptive of the 'sensory design' factors than males. The number of previous visits to the hospital was found to be associated with 'spatial' and 'seating design' factors, while respondents' age had an association with 'sensory' and 'seating design' factors. Respondents ranked 'noise' and 'air freshness' and 'cleanliness' as highly important.
A deeper understanding of building performance is essential to reduce their energy consumption an... more A deeper understanding of building performance is essential to reduce their energy consumption and corresponding greenhouse gas emissions. Heating degree-days (HDD) encapsulates the severity and duration of cold weather, which is routinely used for weather related analysis of fuel consumption, performance benchmarking, and compliance. The accuracy of HDD-based prediction largely depends on the correct base temperature, which varies depending on building thermal characteristics, and their operation and occupancy. We analysed four years' (2012–2016) half-hourly metered gas consumption from 119 non-domestic buildings representing seven types, to: (a) identify their base temperature using a three-parameter change point (3pH) regression model, and (b) their relationships with intrinsic building parameters. The highest mean base temperature, 17.7 • C was found for clubs and community centres, and the lowest, 12.8 • C was for storage buildings. The average of all base temperatures is 16.7 • C, which is 1.2 • C higher and 1.6 • C lower than the British (15.5 • C) and American (18.3 • C) standards respectively. The current practice of a fixed base temperature degree-days for all buildings has been found to be unrealistic. Building type specific base temperatures must be developed, agreed upon and published for increasing accuracy in energy analytics and legislative compliance, as well as for developing effective standards and policies.
Energy prediction models are used in buildings as a performance evaluation engine in advanced con... more Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction – for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.
It is essential to monitor urban evolution at spatial and temporal scales to improve our understa... more It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications.
Recent work has attempted to deliver optimized distributed energy resource management, including ... more Recent work has attempted to deliver optimized distributed energy resource management, including the use of demand side management through smart homes. This aims to reduce power transmission losses, increase the generation share of renewable energy sources and create new markets through peak shaving and flexibility markets. Further, this leverages the development of product models at the device, building, and network level within the operational lifecycle stage, beyond the conventional role of BIM between design and construction stages. However, the management of heterogeneous software entities, incompatible data models and domain perspectives, across systems of systems of significant complexity, represent critical barriers to sustainable urban energy solutions and leads to a highly challenging problem space. The presented work describes a systemic approach based on the concept of holonic systems, which exemplify the role of autonomy, belonging, connectivity, diversity and emergence across entities. This reduces the decision complexity of the problem and facilitates the implementation of optimized solutions in real power systems in a scalable and robust manner. Further, the concept of a flexibility market is introduced, whereby smart appliance owners are able to sell load curtailment and deferment to a local aggregator, which interfaces between a small number of homes and a distribution system operator. Artificial intelligence is present at each of the entities in order to express constraints, trade energy and flexibility, and optimize the network management decisions within that entity's scope. Specifically, this paper focuses on enabling interoperability between system entities such as smart homes, local load aggregators, and last mile network operators. This interoperability is achieved through ontological modelling of the domain, based on the existing standards of CIM, OpenADR, and energy@home. The produced ontology utilizes description logic to formalize the concepts, relationships and properties of the domain. A use case is presented of applying the ontology within a multi-agent system, which enables the optimization of day-ahead markets, load balancing, and stochastic renewable generation, and closely aligns with the holonic approach to deliver a holonic multi-agent system. The use case assumes a scenario in line with the emerging energy landscape of a district of domestic prosumers, with a high penetration of micro-generation, energy storage and electric vehicles. Initial results demonstrate interoperability between heterogeneous agents through ontological modelling based on an integration and extension of existing standards, which acts as a proof of concept for the approach.
Due to rapid urbanization, more than half of the world's population now live in cities. By the ye... more Due to rapid urbanization, more than half of the world's population now live in cities. By the year 2050, the urbanized population will increase to two–thirds of the global population, which prompted increased international attention to identifying environmental, social and economic challenges of urban development encompassing diverse aspects such as energy, water, waste, infrastructure, transportation, public services, and housing. Public perception of the intrinsic indicators is essential to enhance their participation in the process, especially in the developing countries that are undergoing significant changes requiring buy-in from the stakeholders. A nationwide survey (N=620) was carried out in Iraq using a 29-item structured questionnaire to investigate environmental, social and economic challenges of urban development with reference to Iraq. The items were identified through an extensive review of the literature, which was reduced dimensionality using principal component analysis (PCA). In addition to applying statistical tests on the responses to investigate the relationship between the items and demographic characteristics. Seven principal components have been identified, namely, minimize impacts; water, materials and waste; culture and investments; natural hazards; mobility and transportation; and safety. The item Safety of public places was ranked as the most important factor between urban indicators, followed by Water Conservation, Preservation of historic buildings, and Increasing housing projects, respectively. While, the item Earthquakes from the natural hazard group was classified as the least important indicator between all items. The study concluded the necessity to identify environmental, social and economic challenges of urban development in different urban environments, through the investigation of stakeholder perspectives and analysis the urban indicators by adopting computer-based assessment approach for raising the concerns and proving the validity, accuracy, and reliability of the survey data. The local priorities of urban development challenges have been identified that is represent a fundamental step to support communities in making a decision that is considered a very crucial concern for planners, designers, and policy-makers to achieve a healthy environment, social well-being, and economic prosperity toward adopting the long-term sustainability of urban development projects.
Construction projects involve multidisciplinary and multi-actor collaborations that generate mass... more Construction projects involve multidisciplinary and multi-actor collaborations that generate massive amounts of data over their lifecycle. Data are often sensitive, and embody rights, ownership and intellectual property of the creator. Managing project information raises concerns about security, inconsistency and loss of data. Conventional approach of dealing with the complexities of data management involves the adoption of BIM based solutions that lack suitable means for the governance of collaboration, and access and archival of managed data. To overcome the limitations of BIM, Cloud-based governance solutions have been suggested as a way forward. However, there is a lack of understanding of construction ICT (Information and Communication Technology) practices from the perspectives of data management and governance. This paper aims to fill this gap; first, by exploring barriers related to BIM adoption and collaboration practices, in particular, issues related to data management and governance that can potentially be ameliorated with Cloud technologies, and second, by identifying key requirements for Cloud-based BIM governance solutions. A structured questionnaire was conducted among informed construction practitioners in this study. The findings reveal several barriers to BIM adoption alongside ICT and collaboration issues with an urgent need to develop a BIM governance solution underpinned by cloud technology. Further, a number of important requirements for developing BIM governance solutions have been identified.
One of the challenges of the ageing population in many countries is the efficient delivery of hea... more One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expec‑ tancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients' place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost‑effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end‑ to‑end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real‑time monitoring of the environment and occupant behavior using an event‑driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objec‑ tives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks, while the opportunities for integrating environmental factors for analytics and decision‑making, in particular for the long‑term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio‑cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security.
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO... more Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy consumption and associated greenhouse gas emissions from buildings has acted as a catalyst in the increasing installation of meters and sensors for monitoring energy use and indoor environmental conditions in buildings. This paper reviews the state-of-the-art in building energy metering and environmental monitoring, including their social, economic, environmental and legislative drivers. The integration of meters and sensors with existing building energy management systems (BEMS) is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi and BACnet. Findings suggest that energy metering is covered in existing policies and regulations in only a handful of countries. Most of the legislations and policies on energy metering in Europe are in response to the Energy Performance of Buildings Directive (EPBD), 2002/91/EC. However, recent developments in policy are pointing towards more stringent metering requirements in future, moving away from voluntary to mandatory compliance. With regards to metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud – resulting in a range of available solutions, selection of which can be challenging. Developments in communication technologies, in particular in low-power wireless such as ZigBee and Bluetooth LE (BLE), are enabling cost-effective machine to machine (M2M) and internet of things (IoT) implementation of sensor networks. Privacy and data protection, however, remain a concern for data aggregators and end-users. The standardization of network protocols and device functionalities remains an active area of research and development, especially due to the prevalence of many protocols in the BEMS industry. Available solutions often lack interoperability between hardware and software systems, resulting in vendor lock-in. The paper provides a comprehensive understanding of available technologies for energy metering and environmental monitoring; their drivers, advantages and limitations; factors affecting their selection and future directions of research and development – for use a reference, as well as for generating further interest in this expanding research area.
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO... more Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and airconditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hard-coded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.
Understanding the climate and location aspects are usually the first step in energy applications ... more Understanding the climate and location aspects are usually the first step in energy applications – from buildings to renewable energy. With so many of the renewable energy sources being significantly dependent on weather, it is essential that the temporal and geospatial variability and distribution of climatic design parameters are investigated for effective planning and operation. ASHRAE-HOF is the most widely used climatic design conditions database for building energy and HVAC professionals but gap exists in literature on the geospatial and temporal distributions of the HOF dataset. This research explored geospatial distributions of key HOF (2009) climatic parameters: temperature (dry-bulb, wet-bulb, dew-point and mean) and degree-days at various temporal scales. Identified spatial variability illustrate the effects of latitude, elevation, landuse and nearest coastline. Observed trends agree with conventional wisdom; however, sparse coverage in populated areas such as Africa and Asia diminish the versatility of the database. Variations in temperature exist, even between closely spaced sites – emphasizing the need to use location-specific data for enhancing the accuracy of the weather-related analysis. Moreover, latitudinal similarities in the distribution offer potential in identifying candidate locations for reciprocal transfer of knowledge on environmental design and operation.
International Journal of Energy Sector Management, 2009
Purpose: Renewable energy is an important component to the complex portfolio of technologies that... more Purpose: Renewable energy is an important component to the complex portfolio of technologies that have the potential to reduce CO2 emissions and to enhance the security of energy supplies. Despite RE’s potential to reduce CO2 emissions, the expenditure on renewable energy research, development & demonstration (RERD&D) as a percentage of total government energy research, development & demonstration (ERD&D) investment remains low in developed countries. The declining ERD&D expenditure prompted this research to explore the relationship between CO2 emissions per capita and RERD&D as opposed to ERD&D. Methodology: An econometric analysis of annual CO2 emissions per capita during the period 1990 – 2004 for the 15 pre-2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO2 emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country-level GDP per capita and an index of the ratio between industry consumption and industrial production (IICIP) were introduced in the analysis as proxies to control for activities that generate CO2 emissions. A number of panel data econometric models that are able to take into account both country- and time-specific unobserved effects were explored. Findings: It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO2 emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus). Originality: The findings of this paper provided useful insight into the effectiveness of renewable energy RD&D investment in reducing CO2 emissions and are of value in the development of policies for targeted RD&D investment to mitigate the impacts of climate change.
The case put forward by the authors is that the stakeholders need to engage with cultural issues ... more The case put forward by the authors is that the stakeholders need to engage with cultural issues and regional values for success in built environment projects. This collection of scholarly articles addressing the context, expectations and process provide a set of questions and offer ...
Building height is a key geometric attribute for generating 3-D building models. We propose a nov... more Building height is a key geometric attribute for generating 3-D building models. We propose a novel four-stage approach for automated estimation of building heights from their shadows in very high resolution (VHR) multispectral images. First, a building's actual shadow regions are detected by applying ratio-band algorithm to the VHR image. Second, 2-D building footprint geometries are identified using graph theory and morphological fuzzy processing techniques. Third, artificial shadow regions are simulated using the identified building footprint and solar information in the image metadata at predefined height increments. Finally, the difference between the actual and simulated shadow regions at every height increment is computed using Jaccard similarity coefficient. The estimated building height corresponds to the height of the simulated shadow region that resulted in the maximum value for Jaccard index. The algorithm is tested on seven urban sites in Cardiff, U.K. with various levels of morphological complexity. Our method outperforms the past attempts, and the mean error is reduced by at least 21%.
There is a growing interest among healthcare managers and designers in moving towards a 'patient-... more There is a growing interest among healthcare managers and designers in moving towards a 'patient-centred' design of health and care facilities by integrating patient perceptions and expectations of the physical environment where care takes place. Increased interests in physical environments can mostly be attributed to our improved understanding of their role in patients' health outcomes and staff productivity. There is a gap in the literature on users' perspectives on physical settings in the context of healthcare. Moreover, the connection of care services with the design of the facility is often overlooked partly due to the lack of evidence. This research was aimed at filling the gap by exploring outpatients' perspectives on design factors related to the areas frequented by them, e.g., hospital waiting areas. A 16-item questionnaire was conducted among randomly selected outpatients in two hospitals in Qingdao, China, with a response rate of 84.3%. Five principal factors were identified: sensory; lighting and thermal; facilities; spatial; and seating design, which agreed with the literature. Non-parametric tests were applied to assess variances in constructed principal dimensions concerning demographic variables. Female outpatients were found to be more perceptive of the 'sensory design' factors than males. The number of previous visits to the hospital was found to be associated with 'spatial' and 'seating design' factors, while respondents' age had an association with 'sensory' and 'seating design' factors. Respondents ranked 'noise' and 'air freshness' and 'cleanliness' as highly important.
A deeper understanding of building performance is essential to reduce their energy consumption an... more A deeper understanding of building performance is essential to reduce their energy consumption and corresponding greenhouse gas emissions. Heating degree-days (HDD) encapsulates the severity and duration of cold weather, which is routinely used for weather related analysis of fuel consumption, performance benchmarking, and compliance. The accuracy of HDD-based prediction largely depends on the correct base temperature, which varies depending on building thermal characteristics, and their operation and occupancy. We analysed four years' (2012–2016) half-hourly metered gas consumption from 119 non-domestic buildings representing seven types, to: (a) identify their base temperature using a three-parameter change point (3pH) regression model, and (b) their relationships with intrinsic building parameters. The highest mean base temperature, 17.7 • C was found for clubs and community centres, and the lowest, 12.8 • C was for storage buildings. The average of all base temperatures is 16.7 • C, which is 1.2 • C higher and 1.6 • C lower than the British (15.5 • C) and American (18.3 • C) standards respectively. The current practice of a fixed base temperature degree-days for all buildings has been found to be unrealistic. Building type specific base temperatures must be developed, agreed upon and published for increasing accuracy in energy analytics and legislative compliance, as well as for developing effective standards and policies.
Energy prediction models are used in buildings as a performance evaluation engine in advanced con... more Energy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction – for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using out-of-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.
It is essential to monitor urban evolution at spatial and temporal scales to improve our understa... more It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications.
Recent work has attempted to deliver optimized distributed energy resource management, including ... more Recent work has attempted to deliver optimized distributed energy resource management, including the use of demand side management through smart homes. This aims to reduce power transmission losses, increase the generation share of renewable energy sources and create new markets through peak shaving and flexibility markets. Further, this leverages the development of product models at the device, building, and network level within the operational lifecycle stage, beyond the conventional role of BIM between design and construction stages. However, the management of heterogeneous software entities, incompatible data models and domain perspectives, across systems of systems of significant complexity, represent critical barriers to sustainable urban energy solutions and leads to a highly challenging problem space. The presented work describes a systemic approach based on the concept of holonic systems, which exemplify the role of autonomy, belonging, connectivity, diversity and emergence across entities. This reduces the decision complexity of the problem and facilitates the implementation of optimized solutions in real power systems in a scalable and robust manner. Further, the concept of a flexibility market is introduced, whereby smart appliance owners are able to sell load curtailment and deferment to a local aggregator, which interfaces between a small number of homes and a distribution system operator. Artificial intelligence is present at each of the entities in order to express constraints, trade energy and flexibility, and optimize the network management decisions within that entity's scope. Specifically, this paper focuses on enabling interoperability between system entities such as smart homes, local load aggregators, and last mile network operators. This interoperability is achieved through ontological modelling of the domain, based on the existing standards of CIM, OpenADR, and energy@home. The produced ontology utilizes description logic to formalize the concepts, relationships and properties of the domain. A use case is presented of applying the ontology within a multi-agent system, which enables the optimization of day-ahead markets, load balancing, and stochastic renewable generation, and closely aligns with the holonic approach to deliver a holonic multi-agent system. The use case assumes a scenario in line with the emerging energy landscape of a district of domestic prosumers, with a high penetration of micro-generation, energy storage and electric vehicles. Initial results demonstrate interoperability between heterogeneous agents through ontological modelling based on an integration and extension of existing standards, which acts as a proof of concept for the approach.
Due to rapid urbanization, more than half of the world's population now live in cities. By the ye... more Due to rapid urbanization, more than half of the world's population now live in cities. By the year 2050, the urbanized population will increase to two–thirds of the global population, which prompted increased international attention to identifying environmental, social and economic challenges of urban development encompassing diverse aspects such as energy, water, waste, infrastructure, transportation, public services, and housing. Public perception of the intrinsic indicators is essential to enhance their participation in the process, especially in the developing countries that are undergoing significant changes requiring buy-in from the stakeholders. A nationwide survey (N=620) was carried out in Iraq using a 29-item structured questionnaire to investigate environmental, social and economic challenges of urban development with reference to Iraq. The items were identified through an extensive review of the literature, which was reduced dimensionality using principal component analysis (PCA). In addition to applying statistical tests on the responses to investigate the relationship between the items and demographic characteristics. Seven principal components have been identified, namely, minimize impacts; water, materials and waste; culture and investments; natural hazards; mobility and transportation; and safety. The item Safety of public places was ranked as the most important factor between urban indicators, followed by Water Conservation, Preservation of historic buildings, and Increasing housing projects, respectively. While, the item Earthquakes from the natural hazard group was classified as the least important indicator between all items. The study concluded the necessity to identify environmental, social and economic challenges of urban development in different urban environments, through the investigation of stakeholder perspectives and analysis the urban indicators by adopting computer-based assessment approach for raising the concerns and proving the validity, accuracy, and reliability of the survey data. The local priorities of urban development challenges have been identified that is represent a fundamental step to support communities in making a decision that is considered a very crucial concern for planners, designers, and policy-makers to achieve a healthy environment, social well-being, and economic prosperity toward adopting the long-term sustainability of urban development projects.
Construction projects involve multidisciplinary and multi-actor collaborations that generate mass... more Construction projects involve multidisciplinary and multi-actor collaborations that generate massive amounts of data over their lifecycle. Data are often sensitive, and embody rights, ownership and intellectual property of the creator. Managing project information raises concerns about security, inconsistency and loss of data. Conventional approach of dealing with the complexities of data management involves the adoption of BIM based solutions that lack suitable means for the governance of collaboration, and access and archival of managed data. To overcome the limitations of BIM, Cloud-based governance solutions have been suggested as a way forward. However, there is a lack of understanding of construction ICT (Information and Communication Technology) practices from the perspectives of data management and governance. This paper aims to fill this gap; first, by exploring barriers related to BIM adoption and collaboration practices, in particular, issues related to data management and governance that can potentially be ameliorated with Cloud technologies, and second, by identifying key requirements for Cloud-based BIM governance solutions. A structured questionnaire was conducted among informed construction practitioners in this study. The findings reveal several barriers to BIM adoption alongside ICT and collaboration issues with an urgent need to develop a BIM governance solution underpinned by cloud technology. Further, a number of important requirements for developing BIM governance solutions have been identified.
One of the challenges of the ageing population in many countries is the efficient delivery of hea... more One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expec‑ tancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients' place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost‑effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end‑ to‑end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real‑time monitoring of the environment and occupant behavior using an event‑driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objec‑ tives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks, while the opportunities for integrating environmental factors for analytics and decision‑making, in particular for the long‑term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio‑cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security.
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO... more Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy consumption and associated greenhouse gas emissions from buildings has acted as a catalyst in the increasing installation of meters and sensors for monitoring energy use and indoor environmental conditions in buildings. This paper reviews the state-of-the-art in building energy metering and environmental monitoring, including their social, economic, environmental and legislative drivers. The integration of meters and sensors with existing building energy management systems (BEMS) is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi and BACnet. Findings suggest that energy metering is covered in existing policies and regulations in only a handful of countries. Most of the legislations and policies on energy metering in Europe are in response to the Energy Performance of Buildings Directive (EPBD), 2002/91/EC. However, recent developments in policy are pointing towards more stringent metering requirements in future, moving away from voluntary to mandatory compliance. With regards to metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud – resulting in a range of available solutions, selection of which can be challenging. Developments in communication technologies, in particular in low-power wireless such as ZigBee and Bluetooth LE (BLE), are enabling cost-effective machine to machine (M2M) and internet of things (IoT) implementation of sensor networks. Privacy and data protection, however, remain a concern for data aggregators and end-users. The standardization of network protocols and device functionalities remains an active area of research and development, especially due to the prevalence of many protocols in the BEMS industry. Available solutions often lack interoperability between hardware and software systems, resulting in vendor lock-in. The paper provides a comprehensive understanding of available technologies for energy metering and environmental monitoring; their drivers, advantages and limitations; factors affecting their selection and future directions of research and development – for use a reference, as well as for generating further interest in this expanding research area.
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO... more Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and airconditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hard-coded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.
Understanding the climate and location aspects are usually the first step in energy applications ... more Understanding the climate and location aspects are usually the first step in energy applications – from buildings to renewable energy. With so many of the renewable energy sources being significantly dependent on weather, it is essential that the temporal and geospatial variability and distribution of climatic design parameters are investigated for effective planning and operation. ASHRAE-HOF is the most widely used climatic design conditions database for building energy and HVAC professionals but gap exists in literature on the geospatial and temporal distributions of the HOF dataset. This research explored geospatial distributions of key HOF (2009) climatic parameters: temperature (dry-bulb, wet-bulb, dew-point and mean) and degree-days at various temporal scales. Identified spatial variability illustrate the effects of latitude, elevation, landuse and nearest coastline. Observed trends agree with conventional wisdom; however, sparse coverage in populated areas such as Africa and Asia diminish the versatility of the database. Variations in temperature exist, even between closely spaced sites – emphasizing the need to use location-specific data for enhancing the accuracy of the weather-related analysis. Moreover, latitudinal similarities in the distribution offer potential in identifying candidate locations for reciprocal transfer of knowledge on environmental design and operation.
International Journal of Energy Sector Management, 2009
Purpose: Renewable energy is an important component to the complex portfolio of technologies that... more Purpose: Renewable energy is an important component to the complex portfolio of technologies that have the potential to reduce CO2 emissions and to enhance the security of energy supplies. Despite RE’s potential to reduce CO2 emissions, the expenditure on renewable energy research, development & demonstration (RERD&D) as a percentage of total government energy research, development & demonstration (ERD&D) investment remains low in developed countries. The declining ERD&D expenditure prompted this research to explore the relationship between CO2 emissions per capita and RERD&D as opposed to ERD&D. Methodology: An econometric analysis of annual CO2 emissions per capita during the period 1990 – 2004 for the 15 pre-2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO2 emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country-level GDP per capita and an index of the ratio between industry consumption and industrial production (IICIP) were introduced in the analysis as proxies to control for activities that generate CO2 emissions. A number of panel data econometric models that are able to take into account both country- and time-specific unobserved effects were explored. Findings: It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO2 emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus). Originality: The findings of this paper provided useful insight into the effectiveness of renewable energy RD&D investment in reducing CO2 emissions and are of value in the development of policies for targeted RD&D investment to mitigate the impacts of climate change.
The case put forward by the authors is that the stakeholders need to engage with cultural issues ... more The case put forward by the authors is that the stakeholders need to engage with cultural issues and regional values for success in built environment projects. This collection of scholarly articles addressing the context, expectations and process provide a set of questions and offer ...
This study presents the anonymization of consumer data in a district-level smart grid using the k... more This study presents the anonymization of consumer data in a district-level smart grid using the k-anonymity approach. The data utilized in this study covers the demographic information and associated energy consumption of consumers. The anonymization process is implemented at the prosumer level, considering their importance in sharing flexibility and distributed generation at the low voltage grid, and the fact that they need to interact with each other and the grid while keeping their data private. The proposed approach is tested under three anonymization scenarios: prosecutor, journalist and marketer. The smart grid data are investigated mostly under the prosecutor scenario with three risk levels: lowest, medium and highest. The results of the k-anonymity approach are compared to k-map and k-map + k-anonymity. No difference has been found between the three investigated approaches for the selected data set. Since, the aim of the k-anonymity is to not transform the information about any individual record among those k-1 individual, the recorded type and the number of attributes play a key role for the anonymization process. One of the risk is the using continuous attributes in the anonymization process which may cause the information lose in the anonymization process such as near real time energy consumptions. Hence we have focused on to anonymization of the consumers' demographic information, rather than their energy consumption.
Power distribution network management must integrate with demand side management, alongside distr... more Power distribution network management must integrate with demand side management, alongside distributed energy resources, in order to meet sustainability, resilience, and economic challenges through a smart grid approach. This paper presents an implementation of the Universal Smart Energy Framework (USEF) through a multiagent system and a novel semantic web ontology, which aligns and enriches relevant existing standards. USEF provides a common specification of the market processes and information exchange but does not specify the internal reasoning of the different roles involved. The authors explain the systematic design and development process from the requirements of the energy-flexibility value chain to software implementation. The underpinning ontology formalizes a domain perspective which is coherent with existing standards, and is sufficient for the agent-oriented implementation of the mentioned framework. As well as contributing this model as a web ontology artifact, the presented work utilizes metapro-gramming to transform the domain model into a standard agent communication language ontology. The research reported in this paper is expected to lead towards efficient and scalable development of decision support and automation software for smart grids.
Cities are engines of economic prosperity and social development. Rapid urbanization and the impa... more Cities are engines of economic prosperity and social development. Rapid urbanization and the impacts of climate change have resulted in increased vulnerabilities in cities. On the other hand, the increasing proliferation of connected devices and distributed monitoring of the environment around us has opened up an opportunity to transform the way we create and manage cities. Contextual evidence of performance, outcome and efficiency can now be readily collected at a higher resolution to aid multidisciplinary and multi-objective decision-making, enabling optimal evolution of cities against the backdrop of constrained resources and intensified vulnerabilities. This paper first argues that distributed and ubiquitous monitoring is at the heart of smart cities. Insights can be inferred from the gathered data with potential for evidence-based decisions at the required spatial and temporal scales. The paper then discusses the development of a comprehensive but concise frameworks called DICES (data, insights, citizen, evidence and standards) for conceptualizing smart cities. The dimensions of DICES are then translated into a process oriented methodology called SMART (specify, monitor, analyze, resolve and transform) by formalizing key aspects of the smart city process. Generality and scalability of DICES and SMART are demonstrated through the development of REPRO, a risk-and evidence-based platform for resilient and optimal design of buildings and infrastructure in a smart city.
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Papers by Monjur Mourshed
Methodology: An econometric analysis of annual CO2 emissions per capita during the period 1990 – 2004 for the 15 pre-2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO2 emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country-level GDP per capita and an index of the ratio between industry consumption and industrial production (IICIP) were introduced in the analysis as proxies to control for activities that generate CO2 emissions. A number of panel data econometric models that are able to take into account both country- and time-specific unobserved effects were explored.
Findings: It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO2 emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus).
Originality: The findings of this paper provided useful insight into the effectiveness of renewable energy RD&D investment in reducing CO2 emissions and are of value in the development of policies for targeted RD&D investment to mitigate the impacts of climate change.
Methodology: An econometric analysis of annual CO2 emissions per capita during the period 1990 – 2004 for the 15 pre-2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO2 emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country-level GDP per capita and an index of the ratio between industry consumption and industrial production (IICIP) were introduced in the analysis as proxies to control for activities that generate CO2 emissions. A number of panel data econometric models that are able to take into account both country- and time-specific unobserved effects were explored.
Findings: It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO2 emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus).
Originality: The findings of this paper provided useful insight into the effectiveness of renewable energy RD&D investment in reducing CO2 emissions and are of value in the development of policies for targeted RD&D investment to mitigate the impacts of climate change.