Now showing 1 - 10 of 15
  • 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.
    Scopus© Citations 78  435
  • 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.
    Scopus© Citations 14  262
  • Publication
    Review of district-scale energy performance analysis: Outlooks towards holistic urban frameworks
    Over the past few decades, the world has experienced a major population shift towards urban areas resulting in environmental degradation and increased energy consumption. To combat these challenges, energy efficiency measures are being deployed to improve the performance of different entities within urban built environments. However, effective implementation of such measures often requires a holistic approach to account for existing interrelated and complex relationships between entities at the urban scale. This paper presents a distillation of salient facts and approaches for energy performance evaluation of districts. The studies are reviewed in three sections; (1) concepts defining district energy performance, (2) approaches and methodologies for district energy performance evaluation and (3) system interactions between district entities. The state of the art review reveals that several challenges exist in the initial stages of energy performance assessment of districts. The suggested framework in this paper addresses this issue through pre-processing of data related to entities such as transportation systems and buildings. The framework classifies the available information under three potential categories, namely, 'subject and Scope’, ‘Input Data Management’ and ‘Methods’. This categorisation results in easier integration of multidisciplinary aspects of entities involved in district energy performance assessment.
    Scopus© Citations 38  612
  • Publication
    Comparative Analysis of Machine Learning Algorithms for Building Archetypes Development in Urban Building Energy Modeling
    The most common approach for urban building energy modeling (UBEM) involves segmenting a building stock into archetypes. Development Building archetypes for urban scale is a complex task and requires a lot of extensive data. The archetype development methodology proposed in this paper uses unsupervised machine learning approaches to identify similar clusters of buildings based on building specific features. The archetype development process considers four crucial processes of machine learning: data preprocessing, feature selection, clustering algorithm adaptation and results validation. The four different clustering algorithms investigated in this study are KMean, Hierarchical, Density-based, K-Medoids. All the algorithms are applied on Irish Energy Performance Certificate (EPC) that consist of 203 features. The obtained results are then used to compare and analyze the chosen algorithms with respect to performance, quality and cluster instances. The K-mean algorithm preforms the best in terms of cluster formation.
      533
  • 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.
      695
  • Publication
    An Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale
    Buildings play a significant role in driving the urban demand and supply of energy. Research conducted in the urban buildings sector indicates that there is a considerable potential to achieve significant reductions in energy consumption and greenhouse gas emissions. These reductions are possible through retrofitting existing buildings into more efficient and sustainable buildings. Building retrofitting poses a huge challenge for owners and city planners because they usually lack expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper proposes a new methodology based on machine learning algorithms to develop an intelligent knowledge-based recommendation system which has the ability to recommend energy retrofit measures. The proposed methodology is based on the following four steps: archetypes development, knowledge-base development, recommendation system development and building retrofitting or performance analysis. A case study of Irish buildings dataset shows that the proposed system can provide effective energy retrofits recommendation and improve building energy performance.
      336
  • Publication
    Uncertainty Quantification In Predictive Modelling Of Heat Demand Using Reduced-order Grey Box Models
    As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters especially when a modelling process is being performed before construction and commissioning. Therefore, uncertainty quantification is important in assessing and predicting the performance of complex energy systems, especially in absence of adequate experimental or real-world data.The main aim of this research is to formulate an uncertainty framework to identify and quantify different types of uncertainties associated with reduced-order grey box energy models used in heat demand prediction of the building stock. The uncertainties are characterized and then propagated using the Monte-Carlo sampling technique. Results signify the importance of uncertainty identification and propagation within a system and thus, an integrated approach to uncertainty quantification is necessary to maintain the relevance of developed models.
      242
  • Publication
    A data-driven approach for multi-scale building archetypes development
    Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings’ physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions.
      616Scopus© Citations 49
  • 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.
      807
  • Publication
    A generalization approach for reduced order modelling of commercial buildings
    Grey-box techniques can counter the computational inefficiency and resource-intensive nature of the conventional complex white-box models. However, these approaches might tend to be too specific in their application and scalability is limited by network order. To overcome these challenges, this study proposes a generalized approach for selection of reduced-order RC network models for commercial buildings using the peak power consumption characterization. The devised methodology is used to design the RC networks of buildings connected to district heating network at University College Dublin. The close proximity between measured and simulated demand indicate the influence of power demand on RC network selection.
      447Scopus© Citations 8