Scale aware modeling and monitoring of the urban energy chain

With energy modeling at different complexity levels for smart cities and the concurrent data availability revolution from connected devices, a steady surge in demand for spatial knowledge has been observed in the energy sector. This transformation occurs in population centers focused on efficient energy use and quality of life. Energy-related services play an essential role in this mix, as they facilitate or interact with all other city services. This trend is primarily driven by the current age of the Ger.: Energiewende or energy transition, a worldwide push towards renewable energy sources, increased energy use efficiency, and local energy production that requires precise estimates of local energy demand and production. This shift in the energy market occurs as the world becomes aware of human-induced climate change, to which the building stock has a significant contribution (40% in the European Union). At the current rate of refurbishment and building replacement, of the buildings existing in 2050 in the European Union, 75% would not be classified as energy-efficient. That means that substantial structural change in the built environment and the energy chain is required to achieve EU-wide goals concerning environmental and energy policy. These objectives provide strong motivation for this thesis work and are generally made possible by energy monitoring and modeling activities that estimate the urban energy needs and quantify the impact of refurbishment measures. To this end, a modeling library called aEneAs was developed in the scope of this thesis that can perform city-wide building energy modeling. The library performs its tasks at the level of a single building and was a first in its field, using standardized spatial energy data structures that allow for portability from one city to another. For data input, extensive use was made of digital twins provided from CAD, BIM, GIS, architectural models, and a plethora of energy data sources. The library first quantifies primary thermal energy demand and then the impact of refurbishment measures. Lastly, it estimates the potential of renewable energy production from solar radiation. aEneAs also includes network modeling components that consider energy distribution in the given context, showing a path toward data modeling and simulation required for distributed energy production at the neighborhood and district level. In order to validate modeling activities in solar radiation and green façade and roof installations, six spatial models were coupled with sensor installations. These digital twins are included in three experiments that highlight this monitoring side of the energy chain and portray energy-related use cases that utilize the spatially enabled web services SOS-SES-WNS, SensorThingsAPI, and FIWARE. To this author\u27s knowledge, this is the first work that surveys the capabilities of these three solutions in a unifying context, each having its specific design mindset. The modeling and monitoring activity and their corresponding literature review indicated gaps in scientific knowledge concerning data science in urban energy modeling. First, a lack of standardization regarding the spatial scales at which data is stored and used in urban energy modeling was observed. In order to identify the appropriate spatial levels for modeling and data aggregation, scale is explored in-depth in the given context and defined as a byproduct of resolution and extent, with ranges provided for both parameters. To that end, a survey of the encountered spatial scales and actors in six different geographical and cultural settings was performed. The information from this survey was used to put forth a standardized spatial scales definition and create a scale-dependent ontology for use in urban energy modeling. The ontology also provides spatially enabled persistent identifiers that resolve issues encountered with object relationships in modeling for inheritance, dependency, and association. The same survey also reveals two significant issues with data in urban energy modeling. These are data consistency across spatial scales and urban fabric contiguity. The impact of these issues and different solutions such as data generalization are explored in the thesis. Further advancement of scientific knowledge is provided specifically with spatial standards and spatial data infrastructure in urban energy modeling. A review of use cases in the urban energy chain and a taxonomy of the standards were carried out. These provide fundamental input for another piece of this thesis: inclusive software architecture methods that promote data integration and allow for external connectivity to modern and legacy systems. In order to reduce time-costly extraction, transformation, and load processes, databases and web services to ferry data to and from separate data sources were used. As a result, the spatial models become central linking elements of the different types of energy-related data in a novel perspective that differs from the traditional one, where spatial data tends to be non-interoperable / not linked with other data types. These distinct data fusion approaches provide flexibility in an energy chain environment with inconsistent data structures and software. Furthermore, the knowledge gathered from the experiments presented in this thesis is provided as a synopsis of good practices

Performance assessment of urban precinct design: a scoping study

Executive Summary: Significant advances have been made over the past decade in the development of scientifically and industry accepted tools for the performance assessment of buildings in terms of energy, carbon, water, indoor environment quality etc. For resilient, sustainable low carbon urban development to be realised in the 21st century, however, will require several radical transitions in design performance beyond the scale of individual buildings. One of these involves the creation and application of leading edge tools (not widely available to built environment professions and practitioners) capable of being applied to an assessment of performance across all stages of development at a precinct scale (neighbourhood, community and district) in either greenfield, brownfield or greyfield settings. A core aspect here is the development of a new way of modelling precincts, referred to as Precinct Information Modelling (PIM) that provides for transparent sharing and linking of precinct object information across the development life cycle together with consistent, accurate and reliable access to reference data, including that associated with the urban context of the precinct. Neighbourhoods are the ‘building blocks’ of our cities and represent the scale at which urban design needs to make its contribution to city performance: as productive, liveable, environmentally sustainable and socially inclusive places (COAG 2009). Neighbourhood design constitutes a major area for innovation as part of an urban design protocol established by the federal government (Department of Infrastructure and Transport 2011, see Figure 1). The ability to efficiently and effectively assess urban design performance at a neighbourhood level is in its infancy. This study was undertaken by Swinburne University of Technology, University of New South Wales, CSIRO and buildingSMART Australasia on behalf of the CRC for Low Carbon Living

Innovative Approaches to 3D GIS Modeling for Volumetric and Geoprocessing Applications in Subsurface Infrastructures in a Virtual Immersive Environment

As subsurface features remain largely ‘out of sight, out of mind’, this has led to challenges when dealing with underground space and infrastructures and especially so for those working in GIS. Since subsurface infrastructure plays a major role in supporting the needs of modern society, groups such as city planners and utility companies and decision makers are looking for an ‘holistic’ approach where the sustainable use of underground space is as important as above ground space. For such planning and management, it is crucial to examine subsurface data in a form that is amenable to 3D mapping and that can be used for increasingly sophisticated 3D modeling. The subsurface referred to in this study focuses particularly on examples of both shallow and deep underground infrastructures. In the case of shallow underground infrastructures mostly two-dimensional maps are used in the management and planning of these features. Depth is a very critical component of underground infrastructures that is difficult to represent in a 2D map and for this reason these are best studied in three-dimensional space. In this research, the capability of 3D GIS technology and immersive geography are explored for the storage, management, analysis, and visualization of shallow and deep subsurface features

Integrated topological representation of multi-scale utility resource networks

PhD ThesisThe growth of urban areas and their resource consumption presents a significant global challenge. Existing utility resource supply systems are unresponsive, unreliable and costly. There is a need to improve the configuration and management of the infrastructure networks that carry these resources from source to consumer and this is best performed through analysis of multi-scale, integrated digital representations. However, the real-world networks are represented across different datasets that are underpinned by different data standards, practices and assumptions, and are thus challenging to integrate. Existing integration methods focus predominantly on achieving maximum information retention through complex schema mappings and the development of new data standards, and there is strong emphasis on reconciling differences in geometries. However, network topology is of greatest importance for the analysis of utility networks and simulation of utility resource flows because it is a representation of functional connectivity, and the derivation of this topology does not require the preservation of full information detail. The most pressing challenge is asserting the connectivity between the datasets that each represent subnetworks of the entire end-to-end network system. This project presents an approach to integration that makes use of abstracted digital representations of electricity and water networks to infer inter-dataset network connectivity, exploring what can be achieved by exploiting commonalities between existing datasets and data standards to overcome their otherwise inhibiting disparities. The developed methods rely on the use of graph representations, heuristics and spatial inference, and the results are assessed using surveying techniques and statistical analysis of uncertainties. An algorithm developed for water networks was able to correctly infer a building connection that was absent from source datasets. The thesis concludes that several of the key use cases for integrated topological representation of utility networks are partially satisfied through the methods presented, but that some differences in data standardisation and best practice in the GIS and BIM domains prevent full automation. The common and unique identification of real-world objects, agreement on a shared concept vocabulary for the built environment, more accurate positioning of distribution assets, consistent use of (and improved best practice for) georeferencing of BIM models and a standardised numerical expression of data uncertainties are identified as points of development.Engineering and Physical Sciences Research Council Ordnance Surve

BIM and GIS applications in bridge projects : a critical review

In recent years, interest in BIM and GIS applications in civil engineering has been growing. For bridge engineering, BIM/GIS applications such as simulation, visualization, and secondary development have been used to assist practitioners in managing bridge construction and decision-making, including selection of bridge location maintenance decisions. In situ 3D modelling of existing bridges with detailed images from UAV camera has allowed engineers to conduct remote condition assessments of bridges and decide on required maintenance actions. Several studies have investigated the applications of BIM/GIS technology on bridge projects. However, there has been limited focus on reviewing the outcomes of these studies to identify the limitations of BIM and GIS applications on bridge projects. Therefore, the aim of this study was to review the research on BIM/GIS technology applications in bridge projects over the last decade. Using a systematic review process, a total of 90 publications that met the inclusion criteria were reviewed in this study. The review identified the state-of-the-art methods of BIM and GIS applications, respectively, at the planning and design, construction, and operation and maintenance phases of bridge projects. However, the findings point to segregated application of BIM and GIS at all phases of bridge projects. The findings of this study will contribute to guiding practitioners in selecting appropriate BIM and GIS technologies for different aspects of bridge projects

BIM for infrastructure: An overall review and constructor perspective

The subject of building information modelling (BIM) has become a central topic to the improvement of the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the concept is being expanded into domains it was not originally conceived to address. Transitioning BIMinto the domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its use from the constructor perspective to review and analyse the current industry positioning and research state of the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. Fromthe analysis, the related research gapswere identified regarding information integration, alignment of BIM processes to constructor business processes & the effective governance and value of information. From this a unique research strategy utilising a framework for information governance coupled with a graph based distributed data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects