Now showing 1 - 2 of 2
  • 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.
      222Scopus© Citations 47
  • 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.
      147Scopus© Citations 48