Now showing 1 - 10 of 21
  • Publication
    Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations
    Photovoltaic systems are one of the most promising renewable energy technologies for on-site generation. Most of the techno-economic studies consider distributed standalone photovoltaic generation with little consideration of community-based standalone photovoltaic systems. Location-based case studies are required to provide economic and reliable photovoltaic systems to meet the peak loads of residential neighbourhoods in an optimized manner. This paper devises an integrated evaluation methodology; a combination of white-box energy modelling and black box photovoltaic design optimization. This research uses optimization methods to develop a quantitative optimized model for analysing the opportunities of centralized systems to adequately meet the demands of a residential neighbourhood and support the grid. This analysis includes three metrics including the level of the energy production, reliability of system for peak power and finally the capital cost of implementation in residential districts. Results indicate that the size of a centralized photovoltaic installation is less when compared to distributed installations to support a similar single peak load. The required converter size is reduced for the centralized system owing to the reduced system size. Centralized installations require fewer batteries to store surplus energy produced due to increased interaction of energy flows. Centralized installations are economically more viable than distributed ones.
      330Scopus© Citations 17
  • Publication
    Accurate identification of influential building parameters through an integration of global sensitivity and feature selection techniques
    The development of building energy performance simulation models often requires significant time and effort to achieve an acceptable degree of prediction accuracy. As such, energy modelers introduce various simplifications and assumptions that require a high degree of modeling literacy to avoid any errors in energy predictions. Previous studies relate these simplifications to the identification of influential building parameters using engineering judgment techniques that are often subjective and differ based on experts’ opinion. The proposed methodology accurately defines influential and non-influential building parameters to formulate a guideline minimum dataset in the context of residential building energy models. The methodology integrates two feature selection techniques (Bayesian Information Criteria and Least Absolute Shrinkage with Selection Operator) with parametric analysis to determine the set of influential parameters. The study uses Irish residential archetypes to compare and validate the subsets of influential parameters using sensitivity rankings and established validation metrics. The predicted annual energy use lies within 10% of measured data for both subsets of influential parameters. Thereby, energy modelers could significantly reduce the time and effort spent on model development while maintaining the desired accuracy. The formulated datasets represent only influential features and hence, could be used by urban planners and energy policymakers to estimate energy retrofit investment costs, emission reductions and energy savings.
      16Scopus© Citations 16
  • Publication
    Feature assessment frameworks to evaluate reduced-order grey-box building energy models
    With a drive towards achieving an integrated energy system, there is a need for holistic and scalable building modelling approaches for the commercial building stock. Existing grey-box modelling approaches often fail to produce a generalised network structure, which limits the suitability of models for different applications. Furthermore, existing feature assessment frameworks provide limited opportunities to quantify the potential of model characteristics in terms of flexibility, scalability and interoperability. Considering the diversity of the possible characterisation approaches, this study aims to define and assess a set of basic and derived features for reduced-order grey-box models through a generalisable framework that would act as a decision support tool for the identification of appropriate model characteristics. This research proposes an integrated methodology to test and evaluate model features, namely, scalability, flexibility, and interoperability for reduced-order grey-box models and formulates test-cases with the available commercial reference buildings published by the Department of Energy of the United States. The model scalability errors lie between 3.42% and 4.35% that indicates the suitability of implementing a zone level model for model predictions at the whole building level. The model flexibility error decreased from 5.73% to 4.78% when considering a trade-off between accuracy and complexity. These frameworks produce scalable and flexible models that facilitate urban energy modelling of building stocks and subsequent evaluation of retrofit strategies. Furthermore, the devised models aid the implementation of heat demand reduction scenarios in a building cluster to achieve an integrated energy system.
      14Scopus© Citations 19
  • Publication
    GIS-Based Residential Building Energy Modeling at District Scale
    (International Building Performance Simulation Association, 2018-09-12) ; ; ;
    Urban planners often develop strategic sustainable energy planning processes that aim to minimize the overall energy consumption and CO2 emissions of buildings. Planning at such scales could be informed by the use of building energy modeling approaches. However, due to inconsistencies in available urban energy data and a lack of scalable building modeling approaches, a gap persists between building energy modeling and traditional planning practices. This paper develops a methodology based on bottom-up approach for GIS-based residential building energy modeling at a district scale. The methodology is applied to districts in Dublin and modeling results indicate where and what type of buildings have the greatest potential for energy savings throughout the city.
      728
  • Publication
    A generalization approach for reduced order modelling of commercial buildings
    (Informa UK Limited, 2019-07-26) ; ;
    Energy-efficient retrofits have become crucial in building sector as approximately 80% of buildings in developed countries are over 10 years old. Building simulation tools are now being used to provide estimates of energy consumption and implement various models which differ on the basis of enclosed details. Not all of these models are effective in terms of computation and the associated computational costs. This work devises a novel and generalized reduced-order grey-box modelling approach to predict the thermal behaviour of commercial buildings. The generalization approach reduces the order/complexity of model and lays out a general structure to obtain reduced-order models based on easily identifiable building metrics. We also implemented a forward-selection procedure to compare results obtained using a metrics-based approach. The network order obtained using metrics-based approach matches with the network order predicted by the forward selection procedure. The generalized structure would reduce the complexities involved in the dynamic simulation of urban building stock.
      509Scopus© Citations 8
  • Publication
    A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making
    Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.
      468Scopus© Citations 85
  • Publication
    A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings
    Urban planners face significant challenges when identifying building energy efficiency opportunities and developing strategies to achieve efficient and sustainable urban environments. A possible scalable solution to tackle this problem is through the analysis of building stock databases. Such databases can support and assist with building energy benchmarking and potential retrofit performance analysis. However, developing a building stock database is a time-intensive modeling procedure that requires extensive data (both geometric and non-geometric). Furthermore, the available data for developing a building database is sparse, inconsistent, diverse and heterogeneous in nature. The main aim of this study is to develop a generic methodology to optimize urban scale energy retrofit decisions for residential buildings using data-driven approaches. Furthermore, data-driven approaches identify the key features influencing building energy performance. The proposed methodology formulates retrofit solutions and identifies optimal features for the residential building stock of Dublin. Results signify the importance of data-driven retrofit modeling as the feature selection process reduces the number of features in Dublin's building stock database from 203 to 56 with a building rating prediction accuracy of 86%. Amongst the 56 features, 16 are identified to be recommended as retrofit measures (such as fabric renovation values and heating system upgrade features) associated with each energy-efficiency rating. Urban planners and energy policymakers could use this methodology to optimize large-scale retrofit implementation, particularly at an urban scale with limited resources. Furthermore, stakeholders at the local authority level can estimate the required retrofit investment costs, emission reductions and energy savings using the target retrofit features of energy-efficiency ratings.
      431Scopus© Citations 76
  • Publication
    Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis
    The world has witnessed a significant population shift to urban areas over the past few decades. Urban areas account for about two-thirds of the world's total primary energy consumption, of which the urban building sector constitutes a significant proportion approximately 40%. Stakeholders such as urban planners and policy makers face substantial challenges when targeting sustainable energy and climate goals related to the buildings’ sector, i.e. to reduce energy use and associated emissions. Urban energy modeling is one possible solution that leverages limited resources to estimate building energy use and support appropriate policy formation. Over the past few years, there have been only a few review studies on urban building energy modeling approaches. These studies lack an in-depth discussion of the challenges and future research opportunities related to data-driven, reduced-order, and simulation-based modeling methods. This paper proposes Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of approaches, methods and tools used for urban building energy modeling. Furthermore, this paper proposes a generalized framework based on existing literature for different urban energy modeling methods. The aim of this study is to assist urban planners and energy policymakers when choosing appropriate methods to develop and implement in-depth sustainable building energy planning and analysis projects based on limited available resources.
      13Scopus© Citations 122
  • Publication
    Quantifying the scalability of reduced-order grey-box energy models for commercial building stock modeling
    Grey-box models are extensively employed in building energy simulations. However, the grey-box approach often leads to application and stakeholder specific models, for instance, the design approach of greybox modeling for commercial buildings differs on a case by case basis. Often, the network order limits the scalability of these networks. Reduced-order grey-box modeling approaches counter these limitations by achieving a trade off between model complexity and desired accuracy. This study, therefore, formulates a generalized methodology to quantify scalability associated with reduced-order grey-box models for heat demand modeling of commercial buildings. The devised methodology assesses model scalability through (1) scalability feature-definition, (2) model identification, (3) multi-level modeling and (4) KPI identification procedures. This study formulates a test-case of 10 buildings (on university campus) with varied operations to implement the devised methodology. Results indicate that model scalability directly associates with the nature of building operation. Furthermore, similar zone variables can effectively represent an entire building provided that the considered zone pre-dominantly occupies majority of the building’s indoor space.
      21
  • Publication
    Extending IFC to support thermal comfort prediction during design
    (European Council on Computing in Construction, 2019-07-12) ; ; ; ; ;
    During the early design stage, designers often rely on general rules of thumb to make critical decisions about the geometry, construction systems and materials without fully evaluating their effects on indoor thermal environment requirements and constraints. Currently, reviewing a design’s sustainability requires designers to spend a significant amount of time manually extracting Thermal Comfort (TC) data from BIMs because of the tedious nature of this task. This paper is motivated by the absence of a standard method and a schema for extracting the necessary data for an automated TC assessment of building designs. The aim is to generate a reusable and retrievable set of Exchange Requirement’s for BIM-based BTCS to facilitate efficient data extraction and exchanges from design models using the IFC file format. Furthermore, we develop an MVD mechanism that provides a structured framework for the definition and exchange of the target data as a step towards standardisation and production of BTCS related information, the results from which contribute to a proposed MVD. The application of the MVD in building design has the potential to improve the early-stage TC assessment of design alternatives. Further, it could reduce the time required to conduct the assessment, increase the reproducibility of results, and formalises the method used.
      839