Insights from inside the school walls: Contextual data crowdsourcing and feedback mechanisms for UK school stock modelling

The auto-generation of UK school building stock models could facilitate non-domestic carbon emissions tracking. However, contextual fabric and building service data are required to differentiate between asset or operational performance, and these may only be available in situ from building users. Engaging such groups through proposed data crowdsourcing would require robust feedback and data gathering mechanisms to be developed to overcome motivational and informational barriers. This paper describes five stakeholder sessions and a crowdsourcing survey of 139 responses from London schools to better understand these two mechanisms. Aesthetics and budgetary drivers were found to be persistent amongst participants, with a diversity of views on achieving these in practice. This research should inform future data gathering and develop more updated and robust stock refurbishment datasets

Modeling of Photovoltaic-Thermal District Heating with Dual Thermal Modes

Solar photovoltaic thermal (PVT) collectors could be a competitive addition to district heating systems, particularly in areas with high energy density since they simultaneously produce electricity and heat whilst increasing the PV efficiency through cooling. This study presents a new Modelica PVT model, which is used together with EnergyPlus in a co-simulation setup to assess the technical feasibility of solar PVT district heating in new builds. The model has been applied to a block of 12 2-bedroom terraced houses with a 184m2 PVT array on the south facing side of the roof. It was identified that well-designed seasonal PVT heating configurations and control schemes are required to maximise PVT outputs. PVT dual thermal modes occur when the PV is either connected to a load or producing at close to the maximum power point. Integrating the dual modes into a control system could be more economical if heat tariffs were higher than electrical ones when heat demand is greater than the PVT thermal output

A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach

Building performance evaluation is generally carried out through a non-automated process, where computational models are iteratively built and simulated, and their energy demand is calculated. This study presents a computational tool that automates the generation of optimal building designs in respect of their Life Cycle Carbon Footprint (LCCF) and Life Cycle Costs (LCC). This is achieved by an integration of three computational concepts: (a) A designated space-allocation generative-design application, (b) Using building geometry as a parameter in NSGA-II optimization and (c) Life Cycle performance (embodied carbon and operational carbon, through the use of thermal simulations for LCCF and LCC calculation). Examining the generation of a two-storey terrace house building, located in London, UK, the study shows that a set of building parameters combinations that resulted with a pareto front of near-optimal buildings, in terms of LCCF and LCC, could be identified by using the tool. The study shows that 80% of the optimal building’s LCCF are related to the building operational stage (σ = 2), while 77% of the building’s LCC is related to the initial capital investment (σ = 2). Analysis further suggests that space heating is the largest contributor to the building’s emissions, while it has a relatively low impact on costs. Examining the optimal building in terms compliance requirements (the building with the best operational performance), the study demonstrated how this building performs poorly in terms of Life Cycle performance. The paper further presents an analysis of various life-cycle aspects, for example, a year-by-year performance breakdown, and an investigation into operational and embodied carbon emissions

A review of approaches and applications in building stock energy and indoor environment modelling

Current energy and climate policies are formulated and implemented to mitigate and adapt to climate change. To inform relevant building policies, two bottom-up building stock modelling approach: 1) archetype-based and 2) Building-by-building have been developed. This paper presents the main characteristics and applications of these two approaches and evaluates and compares their ability to support policy making. Because of lower data requirements and computational cost, archetype-based modelling approaches are still the mainstream approach to stock-level energy modelling, life cycle assessment, and indoor environmental quality assessment. Building-by-building approaches can better capture the heterogeneous characteristics of each building and are emerging due to the development of data acquisition and computational techniques. The model uncertainties exist in both models which may affect the reliability of outputs, while stochastic archetype models and timeless digital twin model have the potential to address the issue. System dynamics modelling approach can describe and address the dynamics and complexity of often-conflicting policies and achieve co-benefit of multiple policy objectives. This paper aims to provide comprehensive knowledge on building stock modelling for modellers and policymakers, so they could use a building stock model with an appropriate user interface without having to fully understand the underlying algorithms or complexities

Characterising the English school stock using a unified national on-site survey and energy database

The recent commitment towards a net-zero target by 2050 will require considerable improvement to the UK’s building stock. Accounting for over 10% of the services energy consumption of the United Kingdom, the education sector will play an important role. This study aims to improve the understanding of English primary and secondary schools, using national on-site survey data with several large-scale disaggregate data sources. Property Data Survey Programme (PDSP) data on 18,970 schools collected between 2012 and 2014, Display Energy Certificate (DEC) and school census data from the same period were linked and processed to form a unified schools dataset. Statistical analyses were undertaken on 10,392 schools, with a focus on energy performance, and the relationship to several building and system characteristics. The analyses may point to the possibility of assessing operational energy use of schools in a more disaggregate manner. New datasets with detailed and accurate disaggregate information on characteristics of buildings, such as those used in this study, provide opportunities to develop more robust models of the building stock. Such data would provide an opportunity to identify pathways for reducing carbon emissions effectively and provide lessons for other organisations seeking to achieve significant reductions for achieving climate change goals

A linked-data paradigm for the integration of static and dynamic building data in digital twins

Digital Twins is an emerging field of research, mainly because they span the entire building lifecycle promising to uncover hidden inefficiencies and deliver data-driven applications. Broadly defined as real-time digital representations of physical assets, Digital Twins require a connection between static and real-time data. However, building information is usually stored in different formats across the lifecycle, making data integration a challenging task. We hereby often rely on linked data technologies, yet overall system integration approaches with multiple types of data sources. In this work, a data linking methodology is proposed to combine static building design data from Industry Foundation Classes (IFC) and dynamic data using the Brick Schema; a domain ontology which configures data analytics applications during the operational phase. To facilitate this integration, we develop a tool to facilitate the linking of building topology, product, and sensor data using the two schemata. The implementation of our methodology in a real test case demonstrates its significance in combining diverse data sources which can be an important step for the delivery of Digital Twin applications

Developing a Data-driven School Building Stock Energy and Indoor Environmental Quality Modelling Method

The school building sector has a pivotal role to play in the transition to a low carbon UK economy. School buildings are responsible for 15% of the country’s public sector carbon emissions, with space heating currently making up the largest proportion of energy use and associated costs in schools. Children spend a large part of their waking life in school buildings. There is substantial evidence that poor indoor air quality and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and school staff. Maintaining high indoor environmental quality whilst reducing energy demand and carbon emissions in schools is challenging due to the unique operational characteristics of school environments, e.g. high and intermittent occupancy densities or changes in occupancy patterns throughout the year. Furthermore, existing data show that 81% of the school building stock in England was constructed before 1976. Challenges facing the ageing school building stock may be exacerbated in the context of ongoing and future climate change. In recent decades, building stock modelling has been widely used to quantify and evaluate the current and future energy and indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. Building stock models commonly use building archetypes, which aim to represent the diversity of building stocks through frequently occurring building typologies. The aim of this paper is to introduce the Data dRiven Engine for Archetype Models of Schools (DREAMS), a novel, data-driven, archetype-based school building stock modelling framework. DREAMS enables the detailed representation of the school building stock in England through the statistical analysis of two large scale and highly detailed databases provided by the UK Government: (i) the Property Data Survey Programme (PDSP) from the Department for Education (DfE), and (ii) Display Energy Certificates (DEC). In this paper, the development of 168 building archetypes representing 9,551 primary schools in England is presented. The energy consumption of the English primary school building stock was modelled for a typical year under the current climate using the widely tested and applied building performance software EnergyPlus. For the purposes of modelling validation, the DREAMS space heating demand predictions were compared against average measured energy consumption of the schools that were represented by each archetype. It was demonstrated that the simulated fossil-thermal energy consumption of a typical primary school in England was only 7% higher than measured energy consumption (139 kWh/m2/y simulated, compared to 130 kWh/m2/y measured). The building stock model performs better at predicting the energy performance of naturally ventilated buildings,which constitute 97% of the stock, than that of mechanically ventilated ones. The framework has also shown capabilities in predicting energy consumption on a more localised scale. The London primary school building stock was examined as a case study. School building stock modelling frameworks such as DREAMS can be powerful tools that aid decision-makers to quantify and evaluate the impact of a wide range of building stock-level policies, energy efficiency interventions and climate change scenarios on school energy and indoor environmental performance

Modelling platform for schools (MPS): The development of an automated One-By-One framework for the generation of dynamic thermal simulation models of schools

The UK Government has recently committed to achieve net zero carbon status by year 2050. Schools are responsible for around 2% of the UK’s total energy consumption, and around 15% of the UK public sector’s carbon emissions. A detailed analysis of the English school building stock’s performance can help policymakers improve its energy efficiency and indoor environmental quality. Building stock modelling is a technique commonly used to quantify current and future energy demand or indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. ‘Building-by-building’ stock modelling is a modelling technique whereby individual buildings within the stock are modelled and simulated, and performance results are aggregated and analysed at stock level. This paper presents the development of the Modelling Platform for Schools (MPS) – an automated generation of one-by-one thermal models of schools in England through the analysis and integration of a range of data (geometry, size, number of buildings within a school premises etc.) from multiple databases and tools (Edubase/Get Information About Schools, Property Data Survey Programme, Ordanance Survey and others). The study then presents an initial assessment and evaluation of the modelling procedure of the proposed platform. The model evaluation has shown that out of 15,245 schools for which sufficient data were available, nearly 50% can be modelled in an automated manner having a high level of confidence of similarity with the actual buildings. Visual comparison between automatically-generated models and actual buildings has shown that around 70% of the models were, indeed, geometrically accurate

Developing a 3D geometry for Urban energy modelling of Indian cities

The advancement in the field of Urban Building Energy Modelling (UBEM) is assisting urban planners and managers to design and operate cities to meet environmental emission targets. The usefulness of the UBEM depends upon the quality and level of details (LoD) of the inputs to the model. The inadequacy and quality of relevant input data pose challenges. This paper analyses the usefulness of different methodologies for developing a 3D building stock model of Ahmedabad, India, recognizing data gaps and heterogenous development of the city over time. It evaluates the potentials, limitations, and challenges of remote sensing techniques namely (a) Satellite imagery (b) LiDAR and (c) Photogrammetry for this application. Further, the details and benefits of data capturing through UAV assisted Photogrammetry technique for the development of the 3D city model are discussed. The research develops potential techniques for feature detection and model reconstruction using Computer vision on the Photogrammetry reality mesh. Preliminary results indicate that the use of supervised learning for Image based segmentation on the reality mesh detects building footprints with higher accuracy as compared to geometrybased segmentation of the point cloud. This methodology has the potential to detect complex building features and remove redundant objects to develop the semantic model at different LoDs for urban simulations. The framework deployed and demonstrated for the part of Ahmedabad has a potential for scaling up to other parts of the city and other Indian cities having similar urban morphology and no previous data for developing a UBEM

Indoor Air Quality and Overheating in UK Classrooms – an Archetype Stock Modelling Approach

Children spend a large part of their waking lives in school buildings. There is substantial evidence that poor indoor air quality (IAQ) and thermal discomfort can have detrimental impacts on the performance, wellbeing and health of schoolchildren and staff. Maintaining good IAQ while avoiding overheating in classrooms is challenging due to the unique occupancy patterns and heat properties of schools. Building stock modelling has been extensively used in recent years to quantify and evaluate performance of large numbers of buildings at various scales. This paper builds on an archetype stock modelling approach which represents the diversity of the school stock in England through an analysis of The Property Data Survey Programme (PDSP) and the Display Energy Certificates (DEC) databases. The model was used for simulating Indoor-to-Outdoor pollution ratios to estimate indoor air pollution levels (NO2, PM2.5 and CO2) and thermal comfort (overheating) in two climate areas in England: London and the West Pennines. analysis highlighted variations in classrooms' indoor CO2 levels in different seasons and explored the risk of overheating in relation to a classroom's orientation