• Energy Research

The urban building stock dataset consists of synthetic input and output data for the energy simulation of one million buildings. The dataset consists of four different residential types, namely: terraced, detached, semi-detached, and bungalow. Constructing this buildings dataset requires conversion, categorization, extraction, and analytical processes. The dataset (in .csv) format comprises 19 input parameters, including advanced features such as HVAC system parameters, building fabric (walls, roofs, floors, door, and windows) U-values, and renewable system parameters. The primary output parameter in the dataset is Energy Use Intensity (EUI in kWh/(m2*year)), along with Energy Performance Certificate (EPC) labels categorized on an A to G rating scale. Additionally, the dataset contains end-use demand output parameters for heating and lighting, which are crucial output parameters. jEPlus, a parametric tool, is coupled with EnergyPlus and DesignBuilder templates to facilitate physics-based parametric simulations for generating the dataset. The dataset can be a valuable resource for researchers, practitioners, and policymakers seeking to enhance sustainability and efficiency in urban building environments. Furthermore, dataset holds immense potential for future research in the field of building energy analysis and modeling.

Abstract 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.

Abstract 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.

Abstract 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.

AbstractSmart grids provide newer ways of energy production, transmission, and distribution. In a smart grid finer control of electrical devices in household and buildings are implemented to better manage energy demand and supply. However, this finer control commonly known as demand side management (DSM) requires extensive simulation at various levels before a DSM algorithm may actually be deployed in a real building or neighborhood. Since forecasting the energy usage behavior of myriad number of electrical devices is a difficult exercise, simulations are done to assess the effectiveness of a DSM algorithm. The problem with the state-of-the-art simulators is that each is designed for simulating electrical devices’ behavior under specific and limited settings. To this end, we present a highly configurable and extensible smart grid simulator (SGS) that is capable of simulating per-minute granularity of energy usage under numerous settings. Moreover, SGS is able to simulate behavior at four levels: electrical devices, households and buildings, neighborhoods and cities. Given different scenarios SGS can simulate relativistic behavior of energy usage at all four levels.

Abstract The sophistication of building energy performance tools has significantly increased the number of user inputs and parameters used to define energy models. There are numerous sources of uncertainty in model parameters which exhibit varied characteristics. Therefore, uncertainty analysis is crucial to ensure the validity of simulation results when assessing and predicting the performance of complex energy systems, especially in the absence of adequate experimental or real-world data. Furthermore, different kinds of uncertainties are often propagated using similar methods, which leads to a false sense of validity. A comprehensive framework to systematically identify, quantify and propagate these uncertainties is missing. 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 predictions of the building stock. The study introduces an integrated uncertainty approach based on a copula-based theory and nested Fuzzy Monte Carlo approach to address the correlations and separate the different kinds of uncertainties. Nested Fuzzy Monte-Carlo approach coupled with Latin Hypercube Sampling is used to propagate these uncertainties. Results signify the importance of uncertainty identification and propagation within an energy system and thus, an integrated approach to uncertainty quantification is necessary to maintain the relevance of developed building simulation models. Moreover, segregation of relevant uncertainties aids the stakeholders in supporting risk-related design decisions for improved data collection or model improvement.

Abstract 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.

The occupancy profile dataset presented in this study leverages publicly available UK Time Use Survey (TUS) 2014-15 data to evaluate the impact of occupancy on energy consumption at various spatial and temporal scales using multi-scale archetypes. Constructing this occupancy dataset includes conversion, categorisation, extraction and analysis processes. The resulting dataset (in .csv) format represents realistic day-wise zone-level occupancy availability schedules that account for the effect of the type of dwelling, the number of occupants, the month of the year and the day of the week. A total of 5,376 occupancy profiles were extracted, representing a large number of dwellings. These profiles demonstrate the realistic behaviour of occupants' availability in dwellings. These profiles allow us to gain valuable insights into the energy usage patterns in dwellings based on the realistic behaviour of occupants, leading to more accurate and context-specific energy assessments.