Balancing accuracy and computation burden - an evaluation of different sensitivity analysis methods for urban scale building energy models

Urban-scale building energy models capitalise on the increasing accessibility of large-scale urban data sets and allow the rapid evaluation of competing policy options, making them a vital tool for urban responses to the climate emergency. However, the vast number of different inputs required to model a complex urban environment makes it impossible to precisely quantify all inputs and the complex energy flows within models must be simplified to achieve tractable solutions, as a result, the outputs of these models inevitably have a significant range of variation. Without understanding these limits of inference resulting policy advice is inherently defective. Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. Despite a wellestablished body of work applying UA and SA to models of individual buildings, very limited work has been done to apply these tools to urban scale models. This study presents a systematic comparison of three different sensitivity analysis methods for a high resolution, dynamic thermal simulation at the neighbourhood scale. Accuracy, processing time and complexity of application of each method is evaluated to provide guidance which can inform the application of these methods to other urban and large-scale building energy models. The results highlight the importance of considering both model form and input parameter scale when selecting an appropriate method. In this case, the elementary effects method (EER) offers good performance at relatively low simulation cost

A Sociotechnical Perspective on Winter Window Opening and Heating Controls in Purpose-Built Student Accommodation

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. There has been significant investment in purpose-built student accommodation (PBSA) across the UK. A case study research design was used to investigate the in-use performance of two recently built PBSA developments by monitoring indoor environmental quality, radiator use, and window opening, alongside semi-structured interviews with the building’s residents. The results showed that during the heating season the study participants typically controlled the conditions in their bedrooms by opening their windows regularly, often for long periods, and frequently whilst the heating was on. Five behavioural causes of consistent winter window opening were identified. These were to prevent overheating, inadequate ventilation, poor understanding of the controls, lack of responsiveness of the heating system, and lack of financial implications. Important lessons for the future design of PBSA are identified

4DStock: Adding an organisational dimension to a 3D building stock model

Building stock models, such as University College London’s 3DStock, help us understand energy use across a building stock across time and space. 3DStock is currently used for decision-making and evidence gathering at the national and local policy levels in the UK. A novel innovation proposes to add an organisational dimension to the existing 3DStock model, turning it into a 4DStock model. This conceptual paper articulates some of the anticipated benefits and challenges of this effort, introducing why and how three dimensions could become four. The fourth organisational dimension is eventually intended to incorporate trends in building ownership and usership, with a particular focus on non-domestic buildings and commercial real estate. This organisational dimension is critical for setting agendas, creating agreement, and stimulating action because low-carbon technologies do not adopt themselves. By focusing exclusively on physical buildings and premises, stock models generally omit the human dimension of energy use, including ownership and usership. Organisational characteristics are particularly important in commercial real estate (CRE), which includes 50–75 % of the non-domestic building stock. Different sizes and types of building ownership—for example, large/SME; public/private/listed; owner-occupied or tenanted—have been shown to affect the shape and nature of organisational participation in energy efficiency schemes. Different sizes and types of building usership are also important. The concerns, capacities, and conditions of occupiers have been shown to affect their energy practices and cultures. Understanding these dynamics is essential as we move from theoretical models to practical actions. We need a better grip on both ‘achievable potential’ (the subset of technologies that are actually installed in practice) and ‘social potential’ which includes both how these technologies are used and other organisational behaviours. As an initial sketch of this field, the paper concentrates on how a 4DStock model would incorporate both technical and organisational variables related to occupiers. Further developments will be more useful for ongoing carbon accounting and planning in academia, government, and business

Optimising Buildings Retrofits at the Stock Level: The Development of a Methodology

Optimisation has been widely applied to individual building energy models, however there exists a gap in the literature around stock-level optimisation approaches. This paper describes the early development of a novel methodology which aims to allow multi-objective optimisation to be performed over a stock of buildings. This would be valuable to parties such as local authorities who require decision support when mapping retrofit pathways for their stock. An early-stage workflow is described which is facilitated by a custom developed Python module and applied to an EnergyPlus stock model. An optimisation problem can be defined for the area, which influences how the module handles the parametric setup and optimisation. The setup involves specifying restrictions (e.g. listed buildings) and desires for the recommendations (e.g. if a standardised retrofit package should be applied to a certain dwelling type). According to this formulation, the module assists with parameter creation. The EnergyPlus file is automatically altered so that each parameter can be independent. Optimisation objectives and constraints can be derived from the model outputs. The module links EnergyPlus to the NSGA-II algorithm. Results are output in a format that allows easy segmentation, plotting and exporting. The workflow is demonstrated on an area of housing stock to showcase how the methods are informed by an example problem definition. The paper concludes with a discussion around advantages, challenges and future development. The intention is to build on this work and include stages which handle simplification of the stock model, sensitivity analysis and calibration

Pursuing a net-zero carbon future for all: Challenges for commercial real estate

The commercial real-estate industry faces both opportunities and challenges in reaching net-zero carbon goals. Successful low-carbon strategies rely on organizational decision-making, including cooperation between diverse groups of stakeholders. Not all landlords and tenants are equal, so a key challenge will be activating change across the industry as a whole

Data-driven smart buildings: Narratives of drivers and barriers from real-world implementations

Progress in the digitalisation of building services has been slow. Data-driven insights combined with innovative business models have the potential to unlockvalue. Yet barriers associated with implementing smart building technologies in the real worldinclude an unclear value proposition, differing stakeholder perspectives, and limited evidence of the benefits and disbenefits. This paper reports ongoingwork within the International Energy Agency Annex 81, “Data-driven smart buildings, ”to understandthe current technology landscape and opportunities by implementingdata-driven building servicesin non-domestic buildings. Several case studies were collected fromaround the world, contributed by Annex81 participants. This paper discusses stakeholder narratives on the value proposition and lessons learnt from the case studies collected and gives practical suggestions to overcomedigitalisation barriers

Towards a universal access to Urban Building Energy Modelling - The case of low-income, self-constructed houses in informal settlements in Lima, Peru

By 2050 urban population is estimated to grow from 4 billion to almost 7 billion, with over 90% expected in the Global South, where development often takes place as unplanned informal settlements, with essential shortage of critical infrastructure. In processing some of the associated rising challenges, Urban Building Energy Models can play a key role. However, such models have had limited presence in this context, highlighting the inequalities in the representation of such communities in this field. This paper works towards addressing this gap and presents the development of an Urban Building Energy Modelling workflow for analysing the thermal comfort in a self-constructed, low-income housing neighbourhood in Lima, Peru, using an innovative approach, based largely on open source software, such as EnergyPlus, QGIS and Python. The results highlight that the compact and dense built form of the building blocks, can cause higher heat retention, especially in lower thermal zones and therefore result in high indoor temperatures for longer. Additionally, the poor thermal performance of the buildings’ fabric, can cause hourly indoor temperatures to rise to critical levels, especially in higher thermal zones, which can have adverse impacts on the residents’ health. This first step in understanding some of the key issues these communities are facing, is critical in the early assessment of future building retrofit decisions

Getting to net zero: Islington’s social housing stock

This paper describes the development of a detailed plan to get the social housing stock of the Borough of Islington in London, UK, to net zero carbon emissions. This stock is very diverse in form, age and construction, and includes houses, flats and maisonettes. A total of 4500 buildings containing some 33,300 dwellings were modelled using the 3DStock method. Six packages of measures combining fabric improvements, heat pumps and photovoltaic installations were evaluated for each dwelling individually, in terms of costs, the impacts on gas and electricity use, and predicted cuts in carbon emissions. The rollout of measures between 2020 and 2030 was modelled with a specially developed scenario tool, allowing the user to set different criteria and priorities. Fabric measures on their own were shown to achieve only a 13% cut in gas use on average. Heat pumps are the key to displacing gas use. With all measures combined and taking account of the predicted decarbonisation of the electricity supply, it is only possible to achieve an overall 70% cut in emissions by 2030. Policy relevance The development of a detailed practical plan of action is described: an applied case study with the close engagement of the local authority—not a theoretical desk exercise. Each dwelling in Islington’s housing stock was examined and measured separately. The modelling did not rely on ‘archetypes’ as in many such studies. Realistic retrofit options were analysed in each case, using current cost data from practitioners. The same approach could be applied directly to other London boroughs, and for local authorities outside the capital, although different costs and other local factors would apply. For readers outside the UK, the methodology and tools could serve as exemplars. The findings about the respective contributions of heat pumps, solar photovoltaics and fabric measures, and the effects of different priorities in the rollout of retrofits, have relevance for policymaking more generally at local and national levels

An exergy-based simulation stock model: A new approach for policy making

This article presents the use of an exergy-based bottom-up stock model to investigate the impact of large-scale energy retrofit scenarios in the English and Welsh (E&W) non-domestic sector, with a modelling projection to 2050. The model consists of a combination of EnergyPlus as a first law analysis tool and a dynamic exergy analysis method. The aim of the paper is to illustrate the potential of exergy analysis in improving efficiency at a sectoral level. This preliminary study is composed by 6 different large-scale retrofit scenarios including low carbon and low exergy approaches. The results show that current regulations can reduce carbon emissions by up to 50% but only reduce exergy destructions by 8%. On the other hand, a low exergy scenario based on low temperature district systems was able to reduce carbon emissions by 68% and exergy destructions by 26%

Modelling the energy and exergy utilisation of the Mexican non-domestic sector: A study by climatic regions

This paper presents the development of a bottom-up stock model to perform a holistic energy study of the Mexican non-domestic sector. The current energy and exergy flows are shown based on a categorisation by climatic regions with the aim of understanding the impact of local characteristics on regional efficiencies. Due to the limited data currently available, the study is supported by the development of a detailed archetype-based stock model using EnergyPlus as a first law analysis tool combined with an existing exergy analysis method. Twenty-one reference models were created to estimate the electric and gas use in the sector. The results indicate that sectoral energy and exergy annual input are 95.37. PJ and 94.28. PJ, respectively. Regional exergy efficiencies were found to be 17.8%, 16.6% and 23.2% for the hot-dry, hot-humid and temperate climates, respectively. The study concludes that significant potential for improvements still exists, especially in the cases of space conditioning, lighting, refrigeration, and cooking where most exergy destructions occur. Additionally, this work highlights that the method described may be further used to study the impact of large-scale refurbishments and promote national regulations and standards for sustainable buildings that takes into consideration energy and exergy indicators