Options
Hoare, Cathal
Preferred name
Hoare, Cathal
Official Name
Hoare, Cathal
Research Output
Now showing 1 - 6 of 6
- PublicationAn Intelligent Knowledge-based Energy Retrofit Recommendation System for Residential Buildings at an Urban Scale(2018-09-28)
; ; ; ; 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.342 - PublicationGIS-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.696 - PublicationA data-driven approach to optimize urban scale energy retrofit decisions for residential buildings(Elsevier, 2020-01-01)
; ; ; ; ; ; 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.410Scopus© Citations 71 - PublicationExtending 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.816 - PublicationA data-driven approach for multi-scale building archetypes development(Elsevier, 2019-11)
; ; ; ; 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.629Scopus© Citations 50 - PublicationA data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making(Elsevier, 2020-12-01)
; ; ; ; ; ; 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.440Scopus© Citations 80