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The UK government has announced its ten-point plan to annually install 600,000 low-carbon heat pumps by 2028. However, there is a lack of evidence showing potential overheating risk in dwellings retrofitted with heat pumps. This paper examines the prevalence and magnitude of summertime overheating across 24 naturally ventilated social housing dwellings retrofitted with ground source heat pumps (GSHPs). The dwellings are located in a socially deprived area in Oxford (UK). The empirical study included longitudinal monitoring of indoor temperatures in the living rooms and bedrooms during the non-heating seasons of 2021 and 2022 (May–September), which included a record-breaking heatwave in July 2022. Indoor temperature and CO2 levels in bedrooms were monitored across a subset of six dwellings alongside the monitoring of window opening state in three bedrooms to understand the effect of natural ventilation in removing excess heat. About 136 thermal comfort surveys were conducted to ascertain the subjective responses of residents. Overheating risk assessment was carried out using CIBSE static and adaptive methods, which revealed that summertime overheating was prevalent across half of the dwellings in the non-heating season of 2022, as compared to 17% overheated dwellings in 2021. Bungalows with upgraded cavity wall insulation and top floor flats facing south and south-west had a propensity to overheat. The variation in indoor temperature and CO2 levels across a small sample also indicated the relationship between overheating and residents' behaviour. Given that the majority of the dwellings were occupied by retired elderly people with low incomes who are vulnerable to heat and cannot afford active forms of cooling, it is vital to deploy passive design measures, such as appropriate shading devices that are suitable for a heating-dominated climate and enhanced ventilation, as part of home energy retrofits. Implementing reversible heat pumps coupled with solar PVs can provide cooling during heatwaves while delivering low-carbon heat in the winter. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

• 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]

• 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]

Residential demand response using low carbon technologies can potentially offer energy flexibility to the electricity network along with integration of renewable generation. This paper seeks to empirically evaluate the effectiveness of residential demand response trials on a low voltage feeder of a secondary substation in Barnsley (UK). The study used a sample of 14 well-insulated dwellings equipped with home batteries, heat pumps and solar photovoltaic systems coordinated using automated control. Statistical analysis was undertaken using time-series monitoring data obtained at the individual dwelling level, dwelling sample and at the feeder level of the local low voltage network. Resident experience of the trials was assessed through qualitative data obtained from household telephone surveys. Over three weeks of trials, daily demand response interventions of 2 h duration were applied to the sample of 14 dwellings. For evening peak times, the mean reduction in grid electricity import was found to be 1.3 kWh (67%) per dwelling for turn-down interventions which aimed to minimise import. For turn-up interventions between 1 and 3 pm, the mean increase in grid electricity import was found to be 5.8 kWh (645%) per dwelling. The effect of interventions was measured at the low voltage network level for two single-phase feeders, where penetration of trial homes was approximately one-third. A reduction in mean real power up to 21% was observed for turn-down interventions as well as an increase in real mean power up to 307% for turn-up interventions. In general, the trials had little effect on residents in terms of thermal comfort, hot water availability, noise disturbance or disruption to routines, and where such effects were noticed, they were broadly acceptable. The widespread implementation of residential demand response schemes will require increased roll-out of time-of-use tariffs, enhanced resident support and extensive monitoring of low voltage feeders in electricity substations. [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]