Search

• Overheating regulations and calculation methods in 26 European countries were compared. • Most of the existing calculation methods are outdated and do not fit climate-proof buildings. • France requires a mixed-mode operation of naturally ventilated households. • The UK developed a heatwave-based calculation approach. • Comfort-based, multi-zonal, and time-integrated calculation approaches are needed. With the ongoing significance of overheating calculations in the residential building sector, building codes such as the European Energy Performance of Building Directive (EPBD) are essential for harmonizing the indicators and performance thresholds. This paper investigates Europe's overheating calculation methods, indicators, and thresholds and evaluates their ability to address climate change and heat events. e study aims to identify the suitability of existing overheating calculation methods and propose recommendations for the EPBD. The study results provide a cross-sectional overview of twenty-six European countries. The most influential overheating calculation criteria are listed the best approaches are ranked. The paper provides a thorough comparative assessment and recommendations to align current calculations with climate-sensitive metrics. The results suggest a framework and key performance indicators that are comfort-based, multi-zonal, and time-integrated to calculate overheating and modify the EU's next building energy efficiency regulations. The results can help policymakers and building professionals to develop the next overheating calculation framework and approach for the future development of climate-proof and resilient residential buildings. [ABSTRACT FROM AUTHOR]

• EV presents a technical challenge to the grid and an opportunity to introduce sustainable and novel solutions. • A sustainable energy strategy is proposed to exploit the synergy between EV batteries and heat pumps. • Electrification of transport and heat in buildings can reduce typical householder carbon footprint by up to 87%. The demand for electricity has been increasing worldwide and it is predicted that this trend will be particularly reinforced in developing countries by the gradual electrification of the transport sector and heat generation in buildings. The energy supply infrastructure required to meet the additional electricity demand should be carefully managed in light of the climate change carbon emission targets and commitments. A large proportion of the new electricity loads will be met from renewable sources. Therefore, means of power storage become vital to smooth out the intermittent nature of these energy supplies. The introduction of Electric Vehicles (EVs) could provide a viable and dynamic power storage solution through the concept of Vehicle-to-everything (V2X). This involves the storage of renewable energy (RE) in EV batteries during the charging cycle and restitution to the grid (V2G) or homes (V2H) when needed. In this context, this paper presents a methodology involving several strategies to stabilise the grid system and examines the impact of various types of EVs and heat pumps (HPs) for supplying heat in buildings. The results of this research approach show that the synergy of using V2H could reduce the carbon footprint of a typical domestic building in the United Kingdom (UK) by up to 87% and potentially recover up to 21.9 kWh/day of surplus renewable energy. [ABSTRACT FROM AUTHOR]

• Households change their heating patterns when they switch to a heat pump. • Typically, a setback temperature 2–5 °C lower than the desired thermostat setting. • We calculate the increase in heat demand with varied parameters. • The increase in heat demand is determined by heat loss and thermal mass. • This would be a useful metric for 'heat pump readiness'. Heating with air source heat pumps is an important step in the transition to Net Zero Carbon for many households. When predicting the resulting energy consumption it is frequently assumed that heating demand is unchanged from before the switch from gas heating. However, heating patterns usually change, with a net increase in heating demand to achieve the same level of thermal comfort. This paper discusses the change in pattern and uses modelling to determine the resulting increase in energy demand. It shows that the increase can be predicted with information about dwelling heat loss and thermal mass as well as weather and the pre and post switch heating pattern. The increase can be 20% or more for homes with high heat loss. It proposes that the metric for heating demand increase would be a useful measure of heat pump readiness, and that the parameters required to assess this should be provided on energy performance certificates. [ABSTRACT FROM AUTHOR]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

The ability to accurately model and predict timing and duration of activities for different individuals is essential for successful and widespread Demand Side Response (DSR) policies, especially in the residential sector. Understanding what people do during the day and what factors influence their activity participation decisions is important for planning an effective DSR strategy to harness the end-user flexibility. The recent Covid-19 pandemic has shown how much activities can be shifted to a virtual mode in the presence of mobility restrictions. Further, participation in activities via digital devices (virtual activity participation) has spread across society. Such virtual activities, including teleworking, online shopping, and virtual social interactions, are observed to explicitly impact travel behaviour and activity scheduling. And yet, activity-based models of mobility and energy demand do not accommodate the trade-offs between activity types, location and virtual activity participation. This paper presents a model of activity participation that captures the relationship between the three dimensions of: activity type (such as work, study, shopping), activity location (in-home, out-of-home), and activity modality (in-person, virtual). A Multiple-Discrete Continuous Extreme Value (MDCEV) model structure is applied, and the empirical analysis is undertaken using the 2015 United Kingdom Time Use Survey (UKTUS). The model results provide insights for better understanding of the trade-offs made by individuals as they participate in and allocate time across a set of activity type-location-modality alternatives, and the heterogeneity in these trade-offs. Further, holdout sample validation and policy scenario analysis exercises are presented to demonstrate the reliability and suitability of the model for policy implications. The empirical results presented in our paper suggest that this framework embedded in an activity and agent-based simulator of energy demand will enable us to test a variety of scenarios related to end-user flexibility and activity behaviour change, including consideration of virtual activity participation trends. [ABSTRACT FROM AUTHOR]

Decarbonisation of the energy industry and enforcement of strict targets for operational energy consumption means that non-operational greenhouse gases (GHG) emissions, also known as embodied carbon (EC), will soon represent the majority of whole life carbon associated with buildings. EC assessments are often presented as deterministic, single-point values but contain a high degree of variability which is typically unacknowledged. Common sources of uncertainty are variability, data gaps, measurement error and epistemic uncertainty such as absence of detailed material specification (e.g. manufacturer, concrete mix, recycled content etc). Particularly during early design stages when such information is unconfirmed, average material data is used by necessity. While some material databases and LCA software can provide ranges of embodied carbon coefficients (ECC) between some materials and/or the uncertainty within individual manufacturers' carbon data, the practice of reporting this is uncommon and has limited practicality for whole building assessments. This paper presents a simple procedure that selects the highest impact materials of the EC of an asset and implements a Monte-Carlo simulation to estimate the uncertainty behind the product stage EC assessment. Material coefficients of variation (CoV) are obtained from database values where available, and interpolated values are used in the absence of such data. A product stage EC assessment of a UK educational building, initially undertaken using single data points for each material, gave an EC prediction of 525 kgCO 2 e/m2 GIFA. Two scenarios were then assessed using our proposed procedure: 1) the full building scope and 2) substructure and superstructure only. It was demonstrated that, for scenario one, the EC can range from 50 to 140% of the original result when considering the extreme results from the Monte-Carlo simulation. Scenario one (considering the full building scope) resulted in an average EC value (mean ± CoV) of 526 kgCO 2 e/m2 GIFA ± 10.0%. The second scenario (sub- and super-structure only) resulted in an average EC value of 312 kgCO 2 e/m2 GIFA ± 11.9% with a full range of 45–155% of the original result. This paper shows that a straightforward uncertainty analysis procedure can support designers in understanding the possible range of asset product-stage EC and, therefore, inform construction product selections at an early stage where detailed information is not known. The variation also gives a degree of confidence/caution in the average EC prediction in lieu of a single-point result. The construction product CoV results can be used to set target ECCs on projects to help ensure reliable low-carbon products are specified. If these target ECCs were met, a minimum of 29% and 33% (excl. EPD uncertainty) in product stage EC reductions could be achieved. Future work should extend this method to include additional life cycle assessment (LCA) stages and other uncertainty factors. And, the method could be applied to comparative life cycle assessments and optioneering exercises, as well as including more specific construction product variability data. [ABSTRACT FROM AUTHOR]

[Display omitted] • A whole system approach to studying a low-temperature HGSHP system. • 3D coupled TH model with realistic boundaries and dynamic heat pump COP. • Design of a HGSHP system to support a 5th generation DHC network. • Different factors on performance of the HGSHP system and ground thermal behaviour. • Ground moisture transfer, cooling process and buried depth are important. Exploitation of shallow ground and its low-grade heat potential is fundamental to designing 5th generation district heating and cooling (DHC) networks. Horizontal ground-source heat pump (HGSHP) systems are a common way to utilize shallow geothermal energy. Realistic estimation and prediction of performance of a HGSHP system and shallow ground thermal behaviour should consider the whole system including building heating and cooling load, heat pump and ground heat exchanger, and the ground. This should be accompanied by realistic atmospheric and ground conditions. In this paper, a three-dimensional coupled thermal–hydraulic model with realistic boundary conditions adopting a whole system approach is presented. Dynamic heat pump coefficient of performance (COP) that depends on seasonal variation of heating/cooling demand and ground conditions are also considered. Model validations are conducted against experimental and analytical results in literatures. The model is applied for evaluating a HGSHP system to support development of a 5th generation DHC network on a potential site in the UK. Several influencing factors, such as ground moisture transfer, building thermal load mode, buried depth of ground loops, and initial ground temperature profile are studied to assess performance efficiency of the HGSHP system and evolution of ground thermal behaviour in response to heat extraction or rejection into the ground. The results show that 5% of the monthly total heat demand of the site could be met by the designed HGSHP system, consisting of 200 U-shaped ground loops buried at the depth of 3 m and pure water as the heat carrier. Overlooking the ground moisture transfer or hyperbolizing the ground saturation would overestimate the load-carrying capacity of the HGSHP system. The HGSHP system is more efficient with a higher heat pump COP under the heating and cooling mode than under the heating-only mode. Predicted performance of the HGSHP system improves with buried depth of the ground loops. The results also show that a 1 ℃ increase in the undisturbed ground temperature could suffice up to 8% of the monthly total heat demand of the site. [ABSTRACT FROM AUTHOR]

• A well-functioning space heating system can operate below 70 °C for 98% of the year and below 55 °C for 71% of the year. • The performance gap between measured and calculated energy use is widening across building age groups, with a clear increase for post-1980 buildings. • Pre-1980 buildings can operate with supply temperatures below 70 °C for 96–99% of the year and below 55 °C for 67–71% of the year. • Building envelope improvement, whilst recommended, is not a pre-requisite for using low-temperature supply in pre-1980 buildings. • Post-1980 buildings could have limitations in respect of using low-temperature heat under windy conditions. Decarbonising heat in the UK by 2050 will require the wider adoption of low-temperature heat. Current systems, largely relying on gas boilers, have design operating temperatures of 82/71 °C (supply/return) while new standards for 4th Generation District Heating are 55/25 °C. Local authorities must set-up strategies to get their buildings "Heat network ready" but this raises the question of the ability for existing buildings to use low-temperature heat. The aim and the novelty of this paper is to establish a relationship between an energy 'performance gap' in Scottish public buildings and their ability to use low-temperature heat. This performance gap has been evaluated for 121 non-domestic buildings, primarily schools, operated by The City of Edinburgh Council. Space heating system are assumed oversized by 10%. The results show that renovation of the building envelope, while highly desirable, is not a pre-requisite for using low-temperature heat in pre-1980 constructed buildings, which represent 64% of the stock. It also highlights that post-1980 buildings, predominantly utilising mechanical ventilation systems, demonstrate an increasing performance gap which could limit their ability to use reduced operating temperature, especially in windy conditions. [ABSTRACT FROM AUTHOR]

• GB domestic heat pump electricity demand in 2050 is modelled. • Modelling is based on monitored data from heat pumps and gas boilers in homes. • Uptake of heat pumps in all homes more than doubles peak GB electricity demand. • Moderate improvements in HP performance and dwelling fabric reduce peak GB demand. • Flexibility in DHW and space heating provision also reduce peak GB demand. To meet net zero carbon targets by 2050s, countries with cold winters, such as Great Britain (GB), will turn to low carbon domestic heating systems such as electric heat pumps. In this paper, a statistical model is developed using measured performance data from over 550 heat pumps installed in GB homes in 2012. The model is used to predict the additional load on the GB electricity generation and distribution infrastructure for various current and future (2050) climates, dwelling energy efficiencies and heat pump deployment scenarios. For a cold year in the 2020s, a 100% uptake of heat pumps in the existing GB dwelling stock gave a peak electricity demand for the heat pumps of 78 GW and an annual electricity demand of 189 TWh. When added to the other, existing electricity demand, this represents an increase in the GB peak electricity demand in excess of 100% and an annual electricity demand increase of around 60%. Making reasonable assumptions about heat pumps' future efficiency and use, and the future climate and dwelling stock fabric efficiency, and assuming 80% heat pump uptake and no load shifting, the peak heat pump electricity demand for a cold year in the 2050s halved to 37 GW. By shifting demand for water and space heating the 2020s cold year peak demand reduced by 15%. The work provides a more robust estimate of future GB heat pump electricity demands than any previously available. The predicted future ramp rates, peak and annual total electricity demands, are lower than predicted by previous analysts. [ABSTRACT FROM AUTHOR]

Increasing demand for housing is one of the biggest challenges facing the world. Affordable housing is a key priority of the UK government in addressing this challenge, which calls for innovative construction methods to address the issue of fuel poverty at an affordable cost. Timber-based modern methods of construction are one of the key solutions to resolve the existing housing crisis while managing climate change. Therefore, this paper presents a case study of "Integra House", which is a proof of concept of a novel truss technology. The case study is an affordable housing prototype that performs well in both life cycle carbon and cost. The proposed construction uses a novel timber truss technology which makes up the floor, walls and roof of the house, thereby reducing on-site operations and waste while providing a low-carbon low-cost design. The prototype underwent design optimisation and evaluation of options; workshop-based production and performance evaluation of elemental prototypes; production and performance evaluation of a full-scale dwelling prototype; and comparison of capital and life cycle costs and environmental impacts. Further, it underwent a simulation-based optimisation to maximize its performance in cost and carbon by replacing milled timber trusses with whole timber trusses and Rockwool insulation with wood wool insulation. The optimised design option (whole timber) has an EC of 261kgCO 2 per m2 and costs £682 per m2 (excludes substructure, services and fitout) and its operational carbon is 7.9 kgCO 2 per m2/annum and costs £3.30 per m2/annum to operate. Life cycle costs and carbon comparison of the two design prototypes concluded that the whole timber design outperformed the milled timber design in both cost and carbon aspects, by 23% and 30% respectively due to being extremely inexpensive and requiring minimal processing compared to the milled timber option. [ABSTRACT FROM AUTHOR]

• Over the technologies' typical 20-year lifetime, most UK dwellings have a cost optimum low-carbon heating system using air source heat pumps. • Dwelling properties and spatiotemporal variabilities significantly change the optimum low-carbon technology from blue hydrogen boilers, direct electrical heating, and air source heat pumps, to ground source heat pumps. • Direct electrical heating is economically preferred over heat pumps in lower demand dwellings and with analysis over shorter timescales. • Incentives for low-carbon heating need to target adoption of efficient technologies to reduce the greater increase in network demands. An understanding of heating technologies from the consumers' perspective is critical to ensure low-carbon technologies are adopted for reducing their current associated emissions. Existing studies from the consumers' perspective do not compare and optimise the full range and combinations of potential heating systems. There is also little consideration of how spatiotemporal and dwelling variations combined alter the economic and environmental effectiveness of technologies. The novelty of this paper is the creation and use of a new comprehensive framework to capture the range of heating technologies and their viability for any specific dwelling's traits and climate from customers' perspective which is missing from current studies. The model optimises combinations of prime heaters, energy sources, ancillary solar technologies and sizes, thermal energy storage sizes and tariffs with hourly heating simulation across a year and compares their operation, capital, and lifetime costs alongside emissions to realise the true preferential heating systems for customers, which could be used by various stakeholders. Using the UK as a case study, the results show electrified heating is generally the optimum lifetime cost solution, mainly from air source heat pumps coupled with photovoltaics. However, direct electrical heating becomes more economically viable as dwelling demands reduce from smaller dwellings or warmer climates, as shorter durations of the ownership are considered, or with capital cost constraints from lower income households. Understanding this is of high importance, as without correctly targeted incentives, a larger uptake of direct electrical heating may occur, which will burden the electrical network and generation to a greater extent than more efficient heat pumps. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

• An innovative retrofit Motivation-Objective-Criteria (MOC) approach integrating homeowners' engagement is proposed in this paper. • Retrofit motivations are categorised into three typical types: 1) Self-living, 2) Rental or sale, and 3) Investment. • A novel Multi-motivation Performance Factor (MPF) concept is proposed to assess the holistic post-retrofit building performance with comprehensive retrofit combinations. • Homeowners' retrofit priorities have an apparent influence on selecting optimised retrofit measures. • This decision-making supporting tool accelerate building retrofit user acceptance and market penetration According to the EU Commission projection (2016/547/EU), the current average renovation rate is far below the expected rate of 3% to achieve carbon neutrality in building sectors by 2050. This is due to the fact that, during the building retrofit optimisation process, the decision-making criteria and objectives are generally optimised separately, and homeowners' motivations are often ignored or not carefully defined in most research. Limitations existed with a lack of in-depth and comprehensive understanding of the homeowners' motivations in undertaking building retrofit. To address this, we propose an inclusive Motivation-Objective-Criteria (MOC) approach, aiming to bring forward optimised decision-making for building renovation, accommodating different homeowners' retrofit motivations, objectives and criteria. Retrofit motivations are categorised into three typical types: 1) Self-living, 2) Rental or sale, and 3) Investment, under five objectives: Energy Reduction Rate (ERR), Initial Investment (II), Discounted Payback Period (DPP), Bills Reduction Rate (BRR) and Carbon Reduction Rate (CRR). A novel Multi-motivation Performance Factor (MPF) concept is proposed to assess the holistic post-retrofit building performance with comprehensive retrofit combinations. A UK semi-detached house is applied as the reference building to investigate the impact of homeowners' engagement on the decision-making of various retrofit measures. According to the cost-optimal results neglecting homeowners' motivation, it is evident that the mismatch of optimal retrofit combinations occurs between the minimum Initial Investment (II) and Discounted Payback Periods (DPP), with optimal ERR of 72%-79% and 82%-93%, respectively. Comparing cost-optimal with multi-objective optimisation results, it is concluded that homeowners' retrofit priorities have an apparent influence on selecting optimised retrofit measures. Besides, party wall insulation is fundamental for self-living- and rental or sale-motivated types. The solar-assisted heat pump and air–water heat pump are not necessary under the retrofit motivation of the "investment" type. Moreover, the attic floor and external wall insulation are imperative for rental or sale- and investment-motivated types. The sensitivity analysis results are conducted in this research, indicating that the optimal retrofit measures and baseline energy consumption input are in good agreement, with a 10% discrepancy. Thus, the proposed inclusive Motivation-Objective-Criteria approach can incorporate homeowners' engagement in the building retrofitting design process, serving as a decision-making supporting tool to accelerate building retrofit with maximised user acceptance and market penetration. [ABSTRACT FROM AUTHOR]

[Display omitted] • The profile DSR estimation method features a better balance of error compared with previous methods. • Clustering of detailed usage data to create profiles is a viable approach for improving DSR estimation. • Profiles can provide greater account of estimation uncertainty compared to existing deterministic methods. In principle, Demand Side Response (DSR) is increasingly seen as a critical component of a low-carbon electricity network with renewables as main sources of generation. In practice, DSR has been slow to emerge in most electricity markets of developed and developing countries. One of the main reasons for the slow penetration of DSR is the difficulty to assess the flexibility potential of individual sites. This paper develops a new DSR estimation method which uses detailed profiles information based on data on Heating, Ventilation and Air Conditioning (HVAC) chillers in five UK hotels between 2013 and 2017 and applies a combination of clustering analysis and subsequent stochastic sampling using cluster-weighted date-based predictors. Findings show that the profile DSR estimation method features a better balance of error compared with previous methods. [ABSTRACT FROM AUTHOR]