Now showing 1 - 10 of 14
  • 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.
  • 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.
  • Publication
    Dynamic District Information Server: On the Use of W3C Linked Data Standards to Unify Construction Data
    (European Council on Computing in Construction, 2019-07-12) ; ;
    The evolution of ICT and BIM systems in the construc- tion domain yield detailed views of buildings and their use throughout their lifespan. These systems also provide a structure around which information about buildings and their effect on surrounding infrastructure can be described in space and time. Thus, when aggregated, information provided by these systems can serve as a semantic structure through which other information can be stored and con- textualized. While bespoke systems have explored these approaches in particular contexts, few if any systems have been constructed to provide a flexible, semantically rich structure that can be used to structure information about any urban landscape at district and regional scales. This paper describes such a system. The Dynamic District Information Server (DDIS) provides a core information structure which can be extended to store as yet undefined information structures and allow these to be reasoned about in the contexts that range from neighbourhoods to regions. In addition, the paper describes how the DDIS can serve as a coordinating process in a tool chain by providing a semantically rich and flexible notification system that al- lows tools in the chain to notify one another when steps in some information process have been completed.
  • Publication
    Application Of Intelligent Algorithms For Residential Building Energy Performance Rating Prediction
    Energy Performance Certificates (EPC) provide an indication of buildings’ energy use. The creation of an EPC for individual building requires information surveys. Hence, these ratings are typically non-existent for entire building stock. This paper addresses these information gaps using machine-learning models. Developed models were evaluated with Irish EPC data that included approximately 650,000 residential buildings with 199 inputs variables. Results indicate that the deep learning algorithm produces results with highest accuracy level of 88% when only 82 input variables are available. This identified approach will allow stakeholders such as authorities, policy makers and urban-planners to determine the EPC rating for the rest of the building stock using limited data.
  • Publication
    A Framework To Assess The Interoperability Of Commercial Buildings At A District Scale
    (International Building Simulation Association England, 2018-09-12) ; ; ;
    Expensive control technology coupled with absence of a proper framework result in buildings that operate independently for their entire operating life. This paper introduces a framework to assess the potential of buildings to function together using heat load demand patterns and buildings thermal mass. Buildings are characterized as possessing variable and stable heat demand patterns and internal conditions are modified to achieve a peak heat demand reduction. Results indicate 8% reduction in overall peak heat demand when two buildings are operated together. The analysis clearly establishes the significance of an integrated energy system that leads to a reduction in peak loads.
  • 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.
  • 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.
  • 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.
  • Publication
    Development of a Model View Definition (MVD) for thermal comfort analysis in commercial buildings using BIM and EnergyPlus
    (Construction IT Allance of Ireland, 2017-11-24) ; ; ; ;
    Buildings are major consumers of global energy resources. Approximately 80% of the energy used in commercial office spaces, is typically used for maintaining optimal comfort levels through delivery of heating, cooling, ventilating, and lighting. Building Information Modelling (BIM) has seen a significant uptake by designers in pursuit of sustainable building designs. Furthermore, general BIM systems already contain much of the information that can be further reused for additional project tasks such as thermal comfort analysis. Integration and improvement of information flows between BIM and Building Energy Performance Simulation (BEPS) tools has the capacity to help designers assess building performance under various design conditions. In doing so, assessments of building performance and thermal comfort requires additional representative data about indoor environmental conditions and energy consumption. The process of connecting BIM to energy simulation tools, for the explicit purpose of thermal comfort analysis, requires a well-defined Model View Definition (MVD). MVDs define a subset of the Industry Foundation Classes (IFC) schema, which is needed to support a particular business process. This paper develops a MVD for thermal comfort that represents the data needed by building designers or operators to deliver a satisfactory level of thermal comfort in a typical small, single occupant office. The use case consists of a single thermal zone with a HVAC system. The detailed specification for these requirements is based on the IFC data representation. The IfcDoc application tool is used to improve the consistency and define computer-interpretable definition of the MVD. The outputs of this work will allow a standardised exchange of the necessary requirements from BIM to BEPS tools (e.g. EnergyPlus) for thermal comfort analysis.
  • 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.
      182Scopus© Citations 51