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The provision of sustainable housing should factor energy efficiency, the use of sustainable building methods and materials and address the current associated barriers. This consideration triggered an ongoing study in UK which has so far conducted a bibliometric analysis of 275 papers using the VOSviewer software. More papers have been published in the last 5 years compared to the previous 15, and the UK is less engaged in successful collaborations in comparison to other economically developed nations. The lesser use of innovation in the UK has been identified. Also, the uses of sustainable methods and materials in residential housing are not fully exploited for achieving energy efficiency. A future research direction emerging is the potential to study the combined impact of using innovative materials and methods to achieve greater energy efficiency in UK housing provision. Through technological innovations and the need to move from fossil fuels (for example, through the use of heat pumps) a new approach can move the UK closer to achieving net-zero in housing provision and operations. [ABSTRACT FROM AUTHOR]

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the- loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning. [ABSTRACT FROM AUTHOR]

Heat pumps are a solution for decarbonising home heating in the UK. However, the readiness of UK homes for heat pumps is an area of concern regarding the policies aimed at increasing heat pump adoption. This work combines multiple perspectives in evaluating the technical readiness of homes with the market readiness of installers and homeowners to proceed with installing heat pumps. The effectiveness of past heating and energy efficiency policies in the UK are reviewed, along with building regulations, incentives to promote energy efficiency and the effectiveness of heat pump technology in heating homes. Current policies support the cost of a heat pump but home improvements to make homes 'heat pump-ready' can be necessary to achieve optimal heat pump system performance. This paper suggests the UK will face three major challenges. First, analysis highlights an 'eligibility-readiness gap' describing the difference between homes 'eligible' (50%) for the Government's Boiler Upgrade Scheme—a subsidy for heat pump installations—and the likely level of homes that are heat pump-ready (11%) for successful heat pump installations. Second, semi-structured interviews with heat pump installers identified gaps in capacity to deliver the necessary works to make homes heat pump-ready. As small or medium enterprises, the majority of installers do not currently see adding home improvement services to their existing business model as beneficial. All installers highlighted the need for Government to address the cost of electricity relative to gas. Third, a national survey of homeowners in England with gas boilers (n = 1,021) revealed low awareness of the necessary work to make homes heat pump-ready and low willingness to spend money on them unless supported by Government. This paper shows that the processes and costs involved in making homes heat pump-ready before successful design and installation are underappreciated by homeowners, inadequately served by industry, and insufficiently supported by Government. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

Smart meters (SM) with wireless capabilities are one of the most meaningful applications of the Internet of Things. Standards like Zigbee have found a niche in transmitting data on energy usage to the user and the supplier wirelessly via these meters and communication hubs. There are still certain difficulties, notably in delivering wireless connectivity to meters situated in difficult-to-reach locations such as basements or deep indoors. To solve this issue, this paper investigates the usage of mesh networks at 868 MHz, particularly to increase coverage, and proposes an additional mounted antenna to significantly increase outside coverage while providing the necessary coverage extension for hard-to-reach indoor locations. Extensive measurements were made in Newbury in both suburban and open environments for validation and delivery of a simple statistical model for the 868 MHz band in United Kingdom conurbations. Results presented in this paper estimate that mesh networks at 868 MHz can accommodate large areas constituting several SMs with the proposed coverage extension method. With our findings and proposed methods on mesh connectivity, only 1% of UK premises will require mesh radios to achieve the desired coverage. [ABSTRACT FROM AUTHOR]

The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection. [ABSTRACT FROM AUTHOR]

The academic and policy-oriented literature increasingly quantifies the wider effects of energy policy on the macroeconomy. However, the spillovers from economic policies to energy use are less frequently recognised, meaning that many policymakers who strive to improve productivity whilst simultaneously targeting emission reduction fail to consider these interactions. This paper addresses this issue by using simulation results generated by introducing an exogenous increase in labour productivity in a computable general equilibrium model calibrated with data from the United Kingdom. Theoretical analysis suggests that increasing labour productivity can have positive or negative effects on employment levels and energy use. However, the simulation results show that in the context of a developed open economy, improved labour efficiency will increase employment, a key policy objective, but simultaneously increase energy use. A key policy implication is that this work highlights the need for policy frameworks that explicitly acknowledge and quantify the interconnections between national economic policy strategies and energy policy objectives. Secondly, it shows that in practice, policies to reduce carbon emissions need to be strengthened alongside policies to improve productivity successfully implemented. [ABSTRACT FROM AUTHOR]

Reserve power systems are widely used to provide power to critical infrastructure systems in the event of power outages. The reserve power system may be subject to regulation, typically focussing on a strict operational time commitment, but the energy involved in supplying reserve power may be highly variable. For example, if heating or cooling is involved, energy consumption may be strongly influenced by prevailing weather conditions and seasonality. Replacing legacy assets (often diesel generators) with modern technologies could offer potential benefits and services back to the wider electricity system when not in use, therefore supporting a transition to low‐carbon energy networks. Drawing on the Great Britain telecommunications systems as an example, this paper demonstrates that meteorological reanalyses can be used to evaluate capacity requirements to maintain the regulated target of 5‐days operational reserve. Across three case‐study regions with diverse weather sensitivities, infrastructure with cooling‐driven electricity demand is shown to increase energy consumption during summer, thus determining the overall capacity of the reserve required and the availability of 'surplus' capacity. Lower risk tolerance is shown to lead to a substantial cost increase in terms of capacity required but also enhanced opportunities for surplus capacity. The use of meteorological forecast information is shown to facilitate increased surplus capacity. Availability of surplus capacity is compared to a measure of supply–stress (demand‐net‐wind) on the wider energy network. For infrastructure with cooling‐driven demand (typical of most UK telecommunication assets), it is shown that surplus availability peaks during periods of supply–stress, offering the greatest potential benefit to the national electricity grid. [ABSTRACT FROM AUTHOR]

This paper assesses the performance of waste heat recovery from commercial kitchen wastewater in practice. A pilot study of heat recovery from the kitchen at Penrhyn Castle, a tourist attraction in North Wales (UK), is outlined. The pilot heat recovery site was designed and installed, comprising a heat exchanger, recirculation pumps, buffer tank and an extensive temperature/flow monitoring system for performance monitoring of the waste heat recovery system. Continuous monitoring was conducted for a period of 8 months, covering the 2022 tourist season. The recovered heat from the kitchen wastewater preheats the incoming cold freshwater supply and consequently reduces the amount of energy consumed for subsequent water heating. Retrofitting the pilot heat recovery system to the kitchen drains resulted in a heat saving of 240 kWh per month on average, a reduction of 928.8 kg CO2e per year, and a payback period for the investment costs of approximately two years, depending on the cost of energy supply. The presented results illustrate the potential of this form of renewable heat in reducing the carbon footprint of water heating activities in buildings and the hospitality sector. [ABSTRACT FROM AUTHOR]

Governments throughout the EU and the UK face a persistent challenge of satisfying the continual growth in demand for housing and create zero carbon buildings. This paper presents a novel conceptual framework and empirical results to evaluate the potential for Structurally Insulated Panels (SIPs) to address this challenge. Firstly, we present a comprehensive analysis of the energy performance of a SIPs building in the UK to evaluate the potential for such technologies to satisfy the energy and carbon objectives. Using our unique data set we show that SIPs can exceed the necessary energy efficiency standards and reach Net Zero Carbon. Secondly, we introduce game theory as a novel conceptual framework to understand the incentives of the manufacturers of SIPs and UK/EU authorities. This enables us to identify potential sources of incentive conflict, which inhibit the diffusion of such technologies. We demonstrate that it benefits both parties to engage in medium investment, but the inferior under investment scenario can emerge if the UK/EU does not provide leadership and commitment to SIPs technologies. Regardless of the market design in terms of the timing of decisions, the maximum level of support by both the UK/EU and the SIPs manufacturers cannot be achieved. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [ABSTRACT FROM AUTHOR]

The foreseen development of large-scale Offshore Windfarms (OWFs) further from the shore dictates that the OWF transmission system must be optimally designed based on Dynamic Thermal Rating (DTR) in order to fully utilize the intermittent nature of the wind and to keep the offshore wind cost-competitive. In this paper, a comprehensive, DTR-based, two-stage stochastic model is presented, which has been developed for investment decision support for OWF size and HVAC transmission systems. Complex DTR models for all the critical HV components are made fit for the mixed-integer linear programming problem, while accounting for the stochasticity in wind generation and component availability. The main decisions incorporate the discrete size of OWFs, HV subsea export cable cross-sections and ratings for transformers and shunt reactors. For validation, an actual testcase OWF off the east coast of U.K. has been used. Results indicate that DTR-based iterative design of OWF and its transmission components can significantly improve the business case, even though transmission efficiency and energy delivered are not maximum for the optimal design [ABSTRACT FROM AUTHOR]

Offshore wind is in a rapid transitional phase, pushed worldwide by efforts of those to reduce climate change. Wind power is becoming a commercialised, unsubsidised competitive form of low carbon generation of renewable energy. Marketplaces reflect this growing trend with the first introduction of subsidy free bids in a tender for the Dutch and German governments. The analysis of surrounding literature of subsidy free bids and governmental policies revealed that integration of subsidy free bids have been carried out to various extents. Bids like those seen in the German and Dutch governments have been done in accompaniment with supportive policies and measures. For the UK, a possible subsidy free bid could be developed under the Scottish Sectoral Marine Plan. Owing to that, this paper investigates the feasibility of a subsidy free bid for the Scottish government. Utilising the Department for Business, Energy and Industrial Strategy (BEIS) levelised cost of electricity (LCOE) metric were inserted into a detailed excel spreadsheet. This paper calculates multiple financial scenarios under the LCOE metric to provide an insight into the possible scenarios of which different models of subsidy free bids can be implemented. The main parameters associated with the BEIS metric and calculator design were investigated. These included financial cost predictions, discount rate, generational capacity and net capacity factors. The final conclusion of the generated output data, showed it was indeed possible to adopt a subsidy free bid under the current UK contract for difference (CfD) scheme under strict and favourable conditions. [ABSTRACT FROM AUTHOR]

Deploying smart local energy engagement tools (SLEETs) in local energy projects enables users to better observe and control energy, and potentially become active participants in local energy management. Using a cross-project approach, this paper examines the prevalence, effectiveness and inclusiveness of 84 SLEETs deployed in 72 local energy projects in the UK from 2008 to 2018. An original framework for the characterisation of SLEETs was employed, which grouped them into seven types and characterised them in terms of their level of interaction and interface design. Our study shows that information-driven tools were the most popular in community energy groups, while digital energy platforms or interaction tools with numeric interfaces were the most popular in smart local energy system (SLES) initiatives. In contrast, interaction tools with visual interfaces, and tools offering control were found to be less popular. Spatial analysis revealed that SLEETs were mostly deployed in areas with grid constraints (technology), active community energy groups (people) and engaged local authorities (policy). Effective SLEETs were found to stimulate engagement amongst people (social engagement), and between people and technology (operation and control), while inclusive SLEETs enabled the inclusion of vulnerable and low-income households. The acceptance and implementation of SLES initiatives can be enhanced by creating effective and inclusive SLEETs that align with local users' requirements and are supported by local stakeholders in order to foster trust. In future, there is a need to develop appropriate metrics (key performance indicator) or scoring methods to measure the prevalence, effectiveness, and inclusiveness of SLEETs in a consistent manner. [ABSTRACT FROM AUTHOR]

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving. [ABSTRACT FROM AUTHOR]

This paper provides a high-accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES). To determine the potential of domestic DSM for households, a microgrid model of a typical UK residential estate was developed to simulate the impact of RES. The microgrid model comprises 15 UK households with appropriate allocation of washing machines, tumble dryers and dishwashers in accordance with the statistical data. In order to obtain a high-accuracy result, the power consumption of the microgrid model utilises real historical high-resolution data of household energy consumption and RES generation. Thereafter, 40% of distributed wind and solar energy is implemented in the model to produce two individual scenarios. The operation of the white appliances in the model is controlled using a domestic DSM based on a load shifting algorithm. The primary criterion of the DSM considered in this paper is the reduction in energy feedback to the grid in order to decrease the utilisation of the grid and to reduce the transmission losses. The results obtained from the model simulation are compared to the baseline model and discussed with respect to the possible benefits of implementation of domestic DSM under the impact of RES. It has been shown that the self-consumption ratio of the microgrid operating under the DSM is increased by 3% for both scenarios. The model analysis provides highly realistic results which can be used for efficiency assessment of various load shifting methods. [ABSTRACT FROM AUTHOR]

A varied range of applications make use of 3D models nowadays, for instance in urban planning, energy demand studies, solar irradiation, or noise estimation. Acquisition, maintenance, and production of 3D spatial data is costly and laborious, especially at a national level, a great challenge for National Mapping and Cadastral Agencies (NMCAs) – such as Ordnance Survey (OS) for Great Britain. Generalisation is designed to address this challenge, where new datasets are created from a single source by the selection of the desired information and reduction of the amount of detail and data volume. Extensive literature exists in the context of 2D generalisation and automated algorithms exist to remove unwanted detail, however, adding a third dimension complicates the process significantly. Here, a methodology to address this issue is proposed, where the façades of a 3D building are decomposed, rotated, and translated from 3D environment to 2D. Existing automated 2D generalisation operators are applied to building elements and once generalised, they are rotated back to 3D. The outer shell of the resulting generalised 3D building is reconstructed with the independently generalised façade. The results demonstrate a potential flexible, component-based method for 3D generalisation, that could benefit NMCAs. [ABSTRACT FROM AUTHOR]

The way of control and operation of an electrical power system has been changing rapidly with the integration of renewable energy sources (RES). One of the emerging issues that require addressing is the capability of RES to participate in the restoration process upon a total or partial system failure. However, with the continuous shutdown of large-centralised generators, which traditionally provided the black start support together with the variability of RES, the restoration process becomes much more complex. Primarily, the RES should have enough capacity to energise the load at the time of the restoration. Nonetheless, due to significant advantages, there is an increasing trend to use RES to meet the local energy demand by large industrial customers. The flexibility of shifting loads together with the surplus of RE generation could support the system operator during the system energisation process after a blackout. This paper mainly focuses on identifying the capabilities and factors that should be accounted for to participate in the system restoration process by large industrial consumers. The case study conducted on a large-scale steel factory in the UK reveals the possibility of supporting the restoration process under the bottom-up approach. [ABSTRACT FROM AUTHOR]

This paper offers an insight on the liberalization process that took place in the United Kingdom starting with 1990 until now. The electricity market in the United Kingdom made incredible progress in the road to reaching a full level of liberalization where customers are free to choose between suppliers and services. An analysis of the most important indicators for the electricity market evolution such as electricity price, the market share of the largest generator, the energy efficiency indicator shows that the liberalization process brought many advantages starting with 2001. Also, UK has to face new challenges in order to keep the development of the industry on the right path, challenges that are presented in the final part of the paper. [ABSTRACT FROM AUTHOR]

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle's cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory. [ABSTRACT FROM AUTHOR]

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Abstract: Papermaking is known as an energy-intensive, but not always efficient industry. This is at least partly due to the fact that the majority of papermaking technologies and procedures were established at a time when energy was both cheap and plentiful. A considerable fraction of the energy required for papermaking is consumed in removing water by the use of steam in the paper machine’s drying section. It is also known that even a small reduction in steam use can result in a significant reduction in production costs and environmental effects. To date, a great deal of research has been undertaken, aimed at improving the performance of the drying section by making more efficient use of dryer steam. This paper investigates a new approach to reducing thermal energy use in paper making by seeking to enhance the amount of water removed in sections of the machine prior to the drying section. The proposed method is focused on sequential modelling of the effect of vacuums used in the forming section on the thermal energy consumption in the drying section. The primary models explain how different vacuum pressures can affect the flow of water from the sheet in the forming section whilst the secondary models describe the effect of increased drainage on the steam requirements in the dryer. Operational data from a UK paper mill are used to illustrate the proposed method. The models developed can have subsequent application to optimising the use of thermal energy in paper making. [Copyright &y& Elsevier]

Conservation voltage reduction (CVR) has been traditionally applied adopting moderate settings at primary substations and when distributed generation was uncommon. However, as new infrastructure is deployed across European-style medium voltage (MV) and low voltage (LV) networks, driven by increasing photovoltaic (PV) penetration levels, the opportunity arises to develop more advanced CVR schemes. This paper proposes a centralized, three-phase AC optimal power flow (OPF)-based CVR scheme that, using monitoring, on-load tap changers and capacitors across MV and LV, actively manages voltages to minimize energy consumption, even with high PV penetration, while considering MV–LV constraints. To tackle scalability issues brought by discrete variables, a two-stage approach is proposed to solve the OPF as a non-linear programming problem (relaxing integer variables). A process that continuously checks customer voltages is adopted to trigger the optimization only when needed. Moreover, CVR benefits are not only quantified at a network level but also for customers, providing useful insights to policy makers. The proposed control is assessed using a realistic, unbalanced U.K. residential MV–LV network (2,400+ customers) with high PV penetration, and 1-min resolution time-varying profiles and load models. Results demonstrate that the proposed control effectively coordinates voltage regulation in MV and LV levels throughout the day, minimizing energy imports for all customers. [ABSTRACT FROM AUTHOR]

The UK has introduced legislation that requires net-zero greenhouse gas emissions to be achieved by 2050. Improving the energy efficiency of homes is a key objective to help reach this target, and the UK government's Clean Growth Strategy aims to get many homes up to an Energy Performance Certificate (EPC) Band of C by 2035. The relationship between home energy-efficiency and occupant health and wellbeing remains an area of ongoing research. This paper explores the nexus between home energy efficiency, energy consumption and self-reported health-an indicator of the general health and wellbeing of the population. We focus on Greater London through secondary data analysis. Energy-efficiency ratings and air infiltration rates of dwellings, derived from EPCs, were aggregated and matched to local area self-reported health and energy consumption data obtained from the Greater London Authority's (GLA) Lower Layer Super Output Area (LSOA) Atlas database. Our regression model indicates that improving the energy efficiency (SAP) rating by 10 points for a typical home may reduce household gas consumption by around 7% (95% CIs: 2%, 14%). Beta regression finds a positive, but not statistically significant association between median SAP rating and the proportion of the population reporting 'good or very good' health when considering all Greater London LSOAs (z score = 0.60, p value = 0.55). A statistically significant positive association is observed however when repeating the analysis for the lowest income quartile LSOAs (z score = 2.03, p value = 0.04). This indicates that the least well-off may benefit most from home energy efficiency programs. A statistically significant positive association is also observed for the relationship between self-reported health and air infiltration rates (z score = 2.62, p value = 0.01). The findings support existing evidence for the predominantly naturally ventilated UK housing stock, suggesting that home energy efficiency measures provide a co-benefit for occupant health provided that adequate air exchange is maintained. [ABSTRACT FROM AUTHOR]

This paper quantifies the carbon emissions embodied in the bilateral trade between India from the non-Annex-I country list under the Kyoto Protocol (1997) with two of its biggest trade partners from the Annex-II country list, the USA and the UK, by applying the input–output modelling during the years of 2011 to 2014, to evaluate and analyse the conjecture around the carbon leakage. After the calculation of the embodied carbon in these bilateral exports and imports, the study further inspects the roles of important drivers those are influencing the calculated emission imbalances in trade. This study addresses a high emission-intensive energy consumption pattern of India relative to both the USA and the UK which is confirming the possibility of carbon leakage from Indian commodity production sectors and inducing a huge contribution from these bilateral trades into the global emissions. Finally, this study provides evidence from a spatial decomposition analysis that the differences among the emission-intensity of output between India and its major developed country trade partners create the maximum impact on the implied emission imbalances in the bilateral trades. With the anticipation of increased India–USA and the India–UK bilateral trade, this study proposes suggestion to reduce India's emission intensity of output through fuel switching to the less emission-intensive energy options and relieve the pressure on the global environment. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. [ABSTRACT FROM AUTHOR]

Currently, there has been an obvious lack of innovation within poultry houses heating, ventilation and air conditioning (HVAC) system design that deals with both energy efficiency and poultry welfare issues. This paper presents an innovative and renewable heating system for poultry house application to meet the welfare living environment requirement for breeding, to improve the energy efficiency of HVAC system and to decrease fossil fuel consumption and harmful gas emissions. The purpose of this study is to design, develop and test the highly efficient hybrid heating system via an integrated photovoltaic/thermal array with polyethylene heat exchanger coupled to geothermal heat pump system based on the East Midlands' climate conditions in the UK. The numerical model is established based on finite volume method and solved by using Engineering Equation Solver, and a good agreement with less than 15% difference between the numerical and experimental results is achieved. The results indicate that the annual electrical and thermal output are 11867 kWh and 30245 kWh, respectively, which not only could fulfil the poultry house electrical need, but also can provide ~43.5% electricity demand of the heat pump compressor operating. [ABSTRACT FROM AUTHOR]

To contain the spread of Covid-19, governments across the world imposed partial or complete lockdowns. National energy demand decreased in periods of lockdowns; however, as people spent more time at home, residential energy use likely increased. This paper reports the results of a UK survey study (N = 1016 participants) about their energy-use practices during the first lockdown in March 2020. The results indicated that self-reported heating behaviours did not substantially change during lockdown. Regarding appliance use, in particular the duration of usage for televisions and computing equipment has increased and has spread more over the day. Being less able to manage financially was correlated with a greater usage of the smart meter in-home display and a greater attempt to save energy was positively correlated with greater usage of the in-home display, though correlations were small. In summary, the results indicate that home energy-use behaviours, in particular around heating, did not change as much as might have been expected, which might at least partly be explained by the comparatively warm weather during the first lockdown. Corroborating the survey findings with actual energy data is the next essential step to understand findings in more detail. [ABSTRACT FROM AUTHOR]

This paper estimates energy demand in the British domestic and industrial sectors and analyzes the extent to which energy-saving technological change is exogenous, or induced by the energy price. The paper implements models with a linear trend, models making use of the price decomposition of Dargay and Gately (1995a) and the Structural Time Series Models (STSMs) of Harvey (1989). Stochastic trends have been found to be rather important while in neither of the sectors assessed in this study could the hypothesis of symmetric price effects be rejected. Following Hunt and Judge (2005), stochastic trend and asymmetric price effects are found to be substitutes in the industrial sector. In particular we conclude that asymmetric price effects can substitute for the slope in the stochastic trend. Finally, energy consumption in the industrial sector is strongly influenced by price while the effect of price in the domestic sector is markedly smaller. [ABSTRACT FROM AUTHOR]

Carbon emissions, being hazardous, are triggering social concerns which have led to the creation of international treaties to address climate change. Similarly, the United Kingdom under the Climate Change Act (2008) has committed to reducing its greenhouse gas emission by at least 80% over 1990 levels by 2050. However, being the oldest member of the EU states (before Brexit), the UK has the oldest housing stock, which contributes to 45% of its carbon emissions due to the older dwellings. To address this issue low carbon retrofitting is needed. Therefore, this paper seeks to investigate the barriers and enablers to energy efficiency retrofitting in social housing in London, UK based on the perception of experts employed in National and construction companies with an experience that ranges between 6 to 16 years. Initial literature suggested that the problem of energy efficiency retrofitting in the general building stock has been addressed, however little has been reported on its application to social housing. This paper, therefore, groups the barriers and enablers into seven categories that include: financial matters, Technical, IT, Government policy and regulation, social factors (including awareness of the energy efficiency agenda), quality of workmanship and disruption to residents, using literature review, interviews and surveys with key stakeholders within the housing sector, and draws recommendations to enable effective and efficient retrofitting for social housing projects. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to present an evaluation of the in-use energy performance of the Richard Feilden House student accommodation at Queen Mary University, London, UK. The building was completed and occupied in October 2007 and the study was conducted over a 2-year period (January 2008–December 2009). Analysis of the energy audit carried out as part of the study is presented in this paper. The findings suggest that the energy performance of the Richard Feilden House student accommodation is in accordance with its design intent and better than the Good Practice Benchmarks. High levels of insulation, thermal mass, ventilation heat recovery, decentralised electric heating, a greater reliance on internal heat gains for space heating and a proactive building management approach have contributed to this success. However, strategies similar to those adopted by the Passivhaus Standards will be required if future targets of low-carbon buildings are to be met. Currently, there is limited knowledge on the in-use energy performance of student housing in the UK, and while the findings from this study can be helpful for benchmark comparison, more such studies of this building types in the UK are required. [ABSTRACT FROM PUBLISHER]

• 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]

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The benefits of energy efficiency are recognized in multiple socio-economic spheres. Still, the quantitative impact on macroeconomic performance is not fully understood, as modeling tools are not thermodynamically consistent—failing to explicitly include the useful stage of energy flows and/or thermodynamic efficiencies in primary–final–useful energy transformations. Misspecification in the link between energy use and the economy underplays the role of energy use and efficiency in economic growth. In this work, we develop and implement the Macroeconometric Resource Consumption model for Portugal (MARCO-PT), 1960–2014. Based on the post-Keynesian framework developed for the United Kingdom (MARCO-UK), our model explicitly includes thermodynamic energy efficiency, extending the analysis to the useful stage of energy flows. The model's stochastic equations are econometrically estimated. The historical influence of key variables—namely thermodynamic energy efficiency—on economic output is assessed through counterfactual simulations and computation of year-by-year output elasticities. The MARCO-PT model adequately describes the historical behavior of endogenous variables. Although its influence has decreased over time, thermodynamic efficiency has consistently been the major contributor to economic growth between 1960–2014, with an average output elasticity of 0.46. Total useful exergy is also a major contributing factor, with an average output elasticity of 0.29. Both have a higher influence than capital, labor, or other energy variables (final energy, prices). An adequate integration of thermodynamic efficiency is thus crucial for macroeconomic models. [ABSTRACT FROM AUTHOR]

An extensive, publicly available dataset is presented—the LAR Broccoli dataset—which contains 20,000 manually annotated images of broccoli heads captured from a moving tractor at an organic farm in the UK. The dataset contains images of the same row of broccoli heads recorded at 30 frames per second (fps) with three different cameras. Two off-the-shelf, relatively low-cost depth-sensing cameras were used, with the tractor moving at a speed of around 1 km/h, in addition to a webcam, with the tractor moving twice as fast. The utility of the dataset is demonstrated in four ways. First, three different state-of-the-art detector models were trained on the dataset, achieving an overall mean Average Precision (mAP) score of over 95% for the best-performing detector. The results validate the utility of the dataset for the standard task of in-field broccoli head recognition. Second, experiments with transfer learning were conducted, initialised with a smaller pre-trained broccoli detection model, and refined with the LAR Broccoli dataset. Third, we assessed the advantages of transfer learning not only using mAP but also according to time and space requirements for training models, which provides a proxy metric for energy efficiency, a practical consideration for real-world model training. Fourth, the cross-camera generalisation among the three camera systems was compared. The results highlight that testing and training detector models using different camera systems can lead to reduced performance, unless the training set also includes some images captured in the same manner as those in the test set. [ABSTRACT FROM AUTHOR]

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3°C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months. [ABSTRACT FROM AUTHOR]

There has been general recognition within the construction industry that there is a discrepancy between the amount of energy that buildings actually use and what designers considered that they should use. This phenomenon is termed “The Performance Gap” and is normally associated with new buildings. However, existing and older buildings contribute a greater amount of operational carbon. In response to the Performance Gap, CIBSE have developed the TM54 process which is aimed at improving energy estimates at design stage. This paper considers how the TM54 process can also be used to develop energy management procedures for existing buildings. The paper describes an exercise carried out for a university workshop building in which design energy use has been compared with the actual building energy use and standard benchmarks. Moreover, a sensitivity assessment has been carried out using different scenarios based on operation hours of building/equipment, boiler efficiency and impact of climate change. The analysis of these results showed high uncertainty in estimates of energy consumption. If carbon challenges are to be met then improved energy management techniques will require a more systematic approach so that facilities managers can identify energy streams and pinpoint problems, particularly where they have assumed responsibility for existing buildings which often have a legacy of poorly metered fuel consumption. [ABSTRACT FROM AUTHOR]

A substantial number of dwellings in the UK have poor building fabric, leading to higher carbon emissions, fuel expenses, and the risk of cold homes. To tackle these challenges, domestic energy efficiency policies are being implemented. One effective approach is the use of energy models, which enable sensitivity analysis to provide valuable insights for policymakers. This study employed dynamic thermal simulation models for 32 housing archetypes representative of solid-walled homes in the UK to calculate the heat loss and the sensitivity coefficient per building fabric feature, after which a metric Heat Loss Sensitivity (HLS) index was established to guide the selection of retrofit features for each archetype. The building fabric features' inputs were then adjusted to establish both lower and upper bounds, simulating low and high performance levels, to predict the how space heating energy demand varies. The analysis was extended by replicating the process with various scenarios considering climates, window-to-wall ratios, and overshadowing. The findings highlight the external wall as the primary consideration in retrofitting due to its high HLS index, even at high window-to-wall ratios. It was also established that dwelling type is important in retrofit decision-making, with floor and loft retrofits having a high HLS index in bungalows. Furthermore, the analysis underlines the necessity for Standard Assessment Procedure assessors to evaluate loft U-value and air permeability rates prior to implementing retrofit measures, given the significance of these factors in the lower and upper bounds analysis. Researchers globally can replicate the HLS index approach, facilitating the implementation of housing retrofit policies worldwide. [ABSTRACT FROM AUTHOR]

We introduce the BDA dynamics and explore the associated applications in renewable energy sector with a focus on data-driven innovation. Our study draws on the exponential growth of renewable energy initiatives over the last decades and on the paucity of literature to illustrate the use of BDA in the energy industry. We conduct a qualitative field study in the UK with stakeholder interviews and analyse our results using thematic analysis. Our findings indicate that no matter if the importance of the energy sector for 'people's well-being, industrial competitiveness, and societal advancement, old fashioned approaches to analytics for organisational processes are currently applied widely within the energy sector. These are triggered by resistance to change and insufficient organisational knowledge about BDA, hindering innovation opportunities. Furthermore, for energy organisations to integrate BDA approaches, they need to deal with challenges such as training employees on BDA and the associated costs. Overall, our study provides insights from practitioners about adopting BDA innovations in the renewable energy sector to inform decision-makers and provide recommendations for future research. [ABSTRACT FROM AUTHOR]

This research delves into the intricate realm of social housing flat units within tower blocks in Leicester, as a microcosm that serves as a perfect reflection of the larger problem of fuel poverty among social housing systems within the UK. The multifaceted approach intertwines energy efficiency upgrades, indoor comfort, and resident satisfaction. Rooted in a comprehensive methodology, this research seeks to address pressing societal challenges within these architectural projects, from fuel poverty and well-being to environmental sustainability and social justice. Through surveys, interviews, audits, simulations, and detailed analyses of summer and winter thermal performance, this study navigates the complex interplay of factors that influence retrofit success. The findings underscore the transformative potential of comprehensive retrofit measures and the paramount importance of resident engagement while offering a potential holistic checklist for future projects. This research paves the way for future studies encompassing contextual diversity, interdisciplinary collaboration, and long-term impact assessment. As it advances, these findings guide the commitment to fostering positive change, enhancing lives, and contributing to a more sustainable and equitable future in social housing retrofit endeavours. [ABSTRACT FROM AUTHOR]

In this paper, a systematic review of academic literature and policy papers since 2008 is undertaken with an aim of identifying the prevalent energy systems models and tools in the UK. A list of all referenced models is presented and the literature is analysed with regards sectoral coverage and technological inclusion, as well as mathematical structure of models. The paper compares available models using an appropriate classification schema, the introduction of which is aimed at making the model landscape more accessible and perspicuous, thereby enhancing the diversity of models within use. The distinct classification presented in this paper comprises three sections, which specify the model purpose and structure, technological detail and mathematical approach. The schema is not designed to be comprehensive, but rather to be a broad classification with pertinent level of information required to differentiate between models. As an example, the UK model landscape is considered and 22 models are classified in three tables, as per the proposed schema. [ABSTRACT FROM AUTHOR]

Cities constitute three quarters of global energy consumption and the built environment is responsible for significant use of final energy (62%) and greenhouse gas emissions (55%). Energy has now become a strategic issue for local authorities (LAs) and can offer savings when budget cuts have threatened the provision of core services. Progressive LAs are exploring energy savings and carbon reduction opportunities as part of the sustainable and smart city agenda. This paper explores the role of citizens in smart city development as "buildings don't use energy: people do". Citizens have the potential to shape transitions towards smart and sustainable futures. This paper contributes to the growing evidence base of citizen engagement in low carbon smart cities by presenting novel insights and practical lessons on how citizen engagement can help in smart city development through co-creation with a focus on energy in the built environment. A case study of Nottingham in the UK, a leading smart city, is analysed using Arnstein's Ladder of Citizen Participation. Nottingham City Council (NCC) has pledged to keep "citizens at the heart" of its plans. This paper discusses learnings from two EU funded Horizon 2020 projects, REMOURBAN (REgeneration MOdel for accelerating the smart URBAN transformation) and eTEACHER, both of which aimed to empower citizens to reduce energy consumption and co-create smart solutions. Although these two projects are diverse in approaches and contexts, what unites them is a focus on citizen engagement, both face to face and digital. REMOURBAN has seen a "whole house" approach to retrofit in vulnerable communities to improve liveability through energy efficiency. User interaction and co-creation in eTEACHER has provided specifications for technical design of an energy saving App for buildings. eTEACHER findings reflect users' energy needs, understanding of control interfaces, motivations for change and own creative ideas. Citizens were made co-creators in eTEACHER from the beginning through regular communication. In REMOURBAN, citizens had a role in the procurement and bidding process to influence retrofit project proposals. Findings can help LAs to engage demographically diverse citizens across a variety of buildings and communities for low carbon smart city development. [ABSTRACT FROM AUTHOR]

The Smart Meter Rollout Programme in the UK has required energy suppliers to offer new smart meters to customers to provide near real-time energy use information and enable two-way communication between the meter and the central system. The provision was expected to result in meaningful energy reductions, but recent estimates suggest that these reductions may be as low as 2%. This paper contributes to the ongoing debate about the effectiveness of smart meters and in-home energy displays by providing insights on energy feedback perceptions from a series of focus groups with postgraduate consumers. In addition to domestic energy use, the study investigated how participants perceived their energy use at work and how they perceived the energy reduction efforts of their institutions and employers. A laddered and projective methodology was used to more deeply question participant perceptions and reveal their attitudes. The analysis of responses revealed a limited awareness around energy efficiency strategies and opportunities for more visual, mobile, engaging and target-driven interfaces for energy data. The findings also agree with previous observations that environmental concerns are not a key driver of energy reduction behaviours. This was shown by laddered questioning, not to be due to a lack of environmental concern, but rather the perception that reducing energy consumption would have negligible impact. A decade after in-home energy displays enabled a means of providing 'visibility' to 'invisible' energy consumption, little appears to have changed in the perception and experience of energy feedback. [ABSTRACT FROM AUTHOR]

Phase imbalance widely exists in the UK's low voltage (415 V, LV) distribution networks. The imbalances not only lead to insufficient use of LV network assets but also cause energy losses. They lead to hundreds of millions of British pounds each year in the UK. The cost-benefit analyses of phase balancing solutions remained an unresolved question for the majority of the LV networks. The main challenge is data-scarcity – these networks only have peak current and total energy consumption that are collected once a year. To perform a cost-benefit analysis of phase balancing for data-scarce LV networks, this paper develops a customized cluster-wise Gaussian process regression (CGPR) approach. The approach estimates the total cost of phase imbalance for any data-scarce LV network by extracting knowledge from a set of representative data-rich LV networks and extrapolating the knowledge to any data-scarce network. The imbalance-induced cost is then translated into the benefit from phase balancing and this is compared against the costs of phase balancing solutions, e.g., deploying phase balancers. The developed CGPR approach assists distribution network operators (DNOs) to evaluate the cost-benefit of phase balancing solutions for data-scarce networks without the need to invest in additional monitoring devices. [ABSTRACT FROM AUTHOR]

The sophistication in the demand management approaches in both transport and energy sectors and their interaction call for modelling approaches that consider both sectors jointly. For agent-based microsimulation models of travel demand and energy consumption, this implies the necessity to ensure consistent representation of user behaviour with respect to mobility and energy consumption behaviours across the model components. Therefore this paper proposes a microeconomic framework, termed the HOT model (Home, Out-of-home, Travel) grounded in the goods-leisure paradigm, but extended to incorporate emerging activity-travel behaviour patterns and their energy consumption implications. We discuss how the model can be operationalised and embedded within agent-based frameworks with a case study using time use and energy consumption data from the UK. [ABSTRACT FROM AUTHOR]

The winter of 2022/23 has seen large increases in energy prices and in the cost of living in many countries around the world, including Great Britain. Here, we report the results of two surveys, combining cross-sectional and longitudinal analysis, in a sample of about 5400 British households. One survey was conducted early in 2023, the other when participants had signed up to an ongoing research study in the past five years. Thermostat settings were about 1°C lower during the cost-of-living crisis than before, and householders were more likely to turn the heating off when the home was unoccupied. The effort to save energy increased compared to pre-cost-of-living-crisis levels. Using the in-home display more in the cost-of-living crisis than before correlated with greater effort to save energy, supporting the notion that displaying energy data can be a useful tool for energy reductions. Finding it difficult to keep comfortably warm in the home and struggling with meeting heating costs were linked to lower wellbeing, strengthening evidence links between cold, damp, and hard-to-heat homes and negative mental health outcomes. About 40% of respondents lowered the flow temperature of the boiler which might imply that highly tailored information campaigns can be effective in changing behaviour. [ABSTRACT FROM AUTHOR]

Objective: To establish a baseline understanding of whether consuming food with the highest nutritional quality, lowest greenhouse gas emissions (GHGE) and cost differs between different UK demographic and socio-economic population groups. Design: Multiple linear regression models were fitted to evaluate the relationship between predictor socio-demographic variables in this study (i.e. sex, ethnic group, age, BMI and level of deprivation) and the response variables (i.e. consumption of items considered most nutritious, with a low GHGE and price, as a proportion of total items consumed). Setting: The UK. Participants: 1374 adult (18–65 years) participants from the National Diet and Nutrition Survey latest waves 9–11 (2016–2017 and 2018–2019). Results: Based on the total energy consumption in a day, the average diet-based GHGE was significantly higher for participants with a higher BMI. Non-white and most deprived participants spent significantly (P < 0·001) less money per total energy consumption. Participants with a BMI between 18·6 and 39·9 kg/m2 and those living in the least deprived areas consumed a significantly (P < 0·001) higher amount of those items considered the most nutritious, with the lowest GHGE and cost per 100 kcal. Conclusions: Consumption of food with the highest nutritional quality, lowest GHGE and cost in the UK varies among those with different socio-demographic characteristics, especially the deprivation level of participants. Our analysis endorses the consideration of environmental sustainability and affordability, in addition to the consideration of nutritional quality from a health perspective, to make current dietary guidelines more encompassing and equitable. [ABSTRACT FROM AUTHOR]