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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]

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]

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]

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]

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]

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]

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]

Short-term building energy consumption forecasting is vital for energy conservation and emission reduction. However, it is challenging to achieve accurate short-term forecasting of building energy consumption due to its nonlinear and non-stationary characteristics. This paper proposes a novel hybrid short-term building energy consumption forecasting model, SSA-CNNBiGRU, which is the integration of SSA (singular spectrum analysis), a CNN (convolutional neural network), and a BiGRU (bidirectional gated recurrent unit) neural network. In the proposed SSA-CNNBiGRU model, SSA is used to decompose trend and periodic components from the original building energy consumption data to reconstruct subsequences, the CNN is used to extract deep characteristic information from each subsequence, and the BiGRU network is used to model the dynamic features extracted by the CNN for time series forecasting. The subsequence forecasting results are superimposed to obtain the predicted building energy consumption results. Real-world electricity and natural gas consumption datasets of office buildings in the UK were studied, and the multi-step ahead forecasting was carried out under three different scenarios. The simulation results indicate that the proposed model can improve building energy consumption forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]

Upcycling presents one of many opportunities for reducing consumption of materials and energy. Despite recent growth evidenced by increasing numbers of practitioners and businesses based on upcycling, it remains a niche activity and requires scaling up to realise its potential benefits. This paper investigates UK household upcycling in order to develop interventions for scaling up upcycling in the UK. Mixed methods were used in four stages: (a) Interviews to gain insights into UK upcycling; (b) a survey to discover key factors influencing UK upcycling; (c) intervention development based on the synthesis of interviews and survey; and (d) use of a semi-Delphi technique to evaluate and develop initial interventions. The results showed approaches to upcycling (e.g., wood, metal and fabric as frequently used materials, online platforms as frequently used source of materials), context for upcycling (e.g., predominant use of home for upcycling), factors influencing UK upcycling with key determinants (i.e., intention, attitude and subjective norm), important demographic characteristics considering a target audience for interventions (i.e., 30+ females) and prioritised interventions for scaling up (e.g., TV and inspirational media and community workshops as short-term high priority interventions). The paper further discusses implications of the study in terms of development of theory and practice of upcycling. [ABSTRACT FROM AUTHOR]

Apart from numerous technical challenges, the transition towards a carbon-neutral energy supply is greatly hindered by limited economic feasibility of renewable energy sources. This results in their slow and bounded penetration in both commercial and residential sectors that are responsible for over 40% of final energy consumption. This paper aims to demonstrate that combined application of sophisticated planning methodologies at building-level and presents incentive mechanisms for renewables that can result in prosumers, featuring hybrid renewable energy systems (HRES), with economic performance comparable to that of conventional energy systems. The presented research enhances existing planning methodologies by integrating appliance-level demand side management into the decision process and investigates its effect on the planning problem. Moreover, the proposed methodology features an innovative and holistic approach that simultaneously assess electrical and thermal domain in both an isolated and grid-connected context. The analyzed hybrid system consists of solar photovoltaic, wind turbine and battery with thermal supply featuring solar thermal collector and a ground-source heat pump. Overall optimization problem is modeled as a mixed-integer linear program, while ranking of all feasible alternatives is made by the multicriteria decision-making algorithm against several technological, economic, and environmental criteria. A real-life scenario of energy system retrofit for a building in the United Kingdom was employed to demonstrate overall cost savings of 12% in the present market and regulation context. [ABSTRACT FROM AUTHOR]

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes were collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data were collected, the variables available and the representativeness of the sample compared to national estimates. It is intended to be a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it. [ABSTRACT FROM AUTHOR]

Renewable energy sources are rapidly increasing in demand and importance as governments and countries around the globe begin to understand their vital role in reducing climate change. This project aimed to design and create an optimised micro-hydro turbine system for downpipes to harness the currently untapped potential energy from rainwater. Experimental methods were used to determine the magnitude of voltage output available at different rainfall intensities by simulating such flow rates on a hydraulic bench. The viability of this energy to power household appliances was then evaluated, and methods of increasing the voltage output were assessed, such as layering the turbines in a single downpipe or placing multiple downpipes around the building. The study determined that, during average rainfall in the UK, a single turbine could produce a maximum of 7.21 V of DC voltage, or 50.49 V during heavy rainfall—enough energy to power a mobile device charger or a vacuum cleaner, respectively. Therefore, this proves a high potential in rainwater energy harvesting as a renewable energy source. It was also concluded that a positive correlation occurred for both the number of turbines in a downpipe and the number of pipes around the building with the voltage output of the whole system. [ABSTRACT FROM AUTHOR]

One potential opportunity for the decarbonisation of heat supply in the UK is the repurposing of onshore hydrocarbon wells for the production and/or storage of geothermal heat. This paper reports an investigation into the most favourable candidate sites for such repurposing, taking into consideration the available thermal energy outputs and technological options for heat use. A GIS mapping model was generated, combining public domain data on onshore wells and production data from onshore fields, provided by the UK Oil and Gas Authority, with available subsurface temperature data. This model has thus integrated information on location, depth, operational status, and bottom-hole temperature for onshore hydrocarbon wells with production rates from onshore fields in the UK. Of the 2242 onshore hydrocarbon wells thus reported, 560 have the potential to be repurposed, 292 of which are currently operating. Using aggregated water production data for all operating wells in each field, the fields with the greatest potential for geothermal repurposing are ranked. Two of these, the Wytch Farm and Wareham fields, are selected for more detailed analysis. Wytch Farm, the largest onshore oilfield in western Europe, produces water at ~65 °C that might yield a feasible thermal power output of ~90 MW. If an end use could be found where it might substitute for burning of natural gas, the value of this output would be ~£90,000 per day or ~£30 million per year. However, this field is located in a protected landscape where local development would be restricted by planning regulations. The Wareham field is not in a protected landscape, but the low temperature, ~44 °C, and low flow rate limit the scope of potential end uses. Nonetheless, these and the other highly ranked fields have potential heat outputs that are significant compared with other geothermal heat projects, thus offering the possibility of making useful contributions to the decarbonisation of UK energy use. [ABSTRACT FROM AUTHOR]

Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities. [ABSTRACT FROM AUTHOR]

With worldwide activities of carbon neutrality, clean energy is playing an important role these days. Natural gas (NG) is one of the most efficient clean energies with less harmful emissions and abundant reservoirs. This work aims at developing a swarm intelligence-based tool for NG forecasting to make more convincing projections of future energy consumption, combining Extreme Gradient Boosting (XGBoost) and the Salp Swarm Algorithm (SSA). The XGBoost is used as the core model in a nonlinear auto-regression procedure to make multi-step ahead forecasting. A cross-validation scheme is adopted to build a nonlinear programming problem for optimizing the most sensitive hyperparameters of the XGBoost, and then the nonlinear optimization is solved by the SSA. Case studies of forecasting the Natural gas consumption (NGC) in the United Kingdom (UK) and Netherlands are presented to illustrate the performance of the proposed hybrid model in comparison with five other intelligence optimization algorithms and two other decision tree-based models (15 hybrid schemes in total) in 6 subcases with different forecasting steps and time lags. The results show that the SSA outperforms the other 5 algorithms in searching the optimal parameters of XGBoost and the hybrid model outperforms all the other 15 hybrid models in all the subcases with average MAPE 4.9828% in NGC forecasting of UK and 9.0547% in NGC forecasting of Netherlands, respectively. Detailed analysis of the performance and properties of the proposed model is also summarized in this work, which indicates it has high potential in NGC forecasting and can be expected to be used in a wider range of applications in the future. [ABSTRACT FROM AUTHOR]

Domestic heating accounts for 64% of domestic energy usage in the UK, yet there are currently very few viable options for low carbon residential heating. The government's carbon plan commits to improving the uptake of district heating connections in new build dwellings, but the greatest carbon saving can be made through targeting traditional housing stock. This paper aims to quantify the potential carbon and energy savings that can be made by connecting a traditional tenement building to a district heating scheme. The study uses a transient system simulation tool (TRNSYS) model to simulate the radiator system in a tenement block and shows that a significant benefit can be achieved by reducing the supply temperature; however, the minimum supply temperature is drastically limited by the building condition. Therefore, the study also critically compares the benefits of a lower supply temperature against minor refurbishments. It was found that improving building conditions alone could offer a 30% reduction in space heating energy consumption, while building improvements and integration of a river source heat pump could offer almost a 70% reduction. It is the recommendation of this study that a dwelling be improved as much as economically possible to achieve the greatest carbon and energetic savings. [ABSTRACT FROM AUTHOR]

Building fabric retrofitting is an important first step in improving building energy efficiency. The United Kingdom's (UK) housing stock is one of the most inefficient in Europe, and Northern Ireland has the second-highest level of fuel poverty in the UK. This Northern Irish case study developed three fabric retrofit scenarios that estimate potential demand reductions, CO2 emissions removals and retrofit costs. The first scenario reduces domestic demand by 10% and removes 6% of domestic emissions. The second scenario is more ambitious than the first, and results in an 18% reduction in demand and 12% of emissions removed. The third scenario proposes fabric retrofitting to PassivHaus standard and results in a 42% reduction in demand and 27% of emissions removed. Furthermore, retrofit schemes can provide up to approximately 350,000 jobs annually between 2022 and 2050 for the Northern Irish population. This study demonstrates how fabric retrofit scenarios can be streamlined to the unique features of a housing stock. It shows that fabric retrofit research is important for the formulation of energy efficiency policy and emphasises that domestic sector retrofitting will yield socioeconomic and environmental benefits locally and internationally. [ABSTRACT FROM AUTHOR]

Countries' emission reduction commitments under the Paris Agreement have significant implications for lifestyles. National planning to meet emission targets is based on modelling and analysis specific to individual countries, whereas global integrated assessment models provide scenario projections in a consistent framework but with less granular output. We contribute a novel methodology for translating global scenarios into lifestyle implications at the national and household levels, which is generalisable to any service or country and versatile to work with any model or scenario. Our 5Ds method post-processes Integrated Assessment Model projections of sectoral energy demand for the global region to derive energy-service-specific lifestyle change at the household level. We illustrate the methodology for two energy services (mobility, heating) in two countries (UK, Sweden), showing how effort to reach zero carbon targets varies between countries and households. Our method creates an analytical bridge between global model output and information that can be used at national and local levels, making clear the lifestyle implications of climate targets. [ABSTRACT FROM AUTHOR]

Globally and regionally, there is an increasing impetus to electrify the road transport system. The diversity and complexity of the road transport system pose several challenges to electrification in sectors that have higher energy usage requirements. Electric road systems (ERS) have the potential for a balancing solution. An ERS is not only an engineering project, but it is also an innovation system that is complex and composed of multiple stakeholders, requiring an interdisciplinary means of aligning problems, relations, and solutions. This study looked to determine the political, economic, social, and technical (PEST) factors by actively engaging UK stakeholders through online in-depth and semi-structured discussions. The focus is on dynamic wireless power transfer (DWPT) due to its wider market reach and on the basis that a comprehensive review of the literature indicated that the current focus is on the technical challenges and hence there is a gap in the knowledge around application requirements, which is necessary if society is to achieve its goals of electrification and GHG reduction. Qualitative analysis was undertaken to identify factors that are critical to the success of a DWPT system. The outcome of this study is knowledge of the factors that determine the function and market acceptance of DWPT. These factors can be grouped into six categories: vehicle, journey, infrastructure, economic, traffic and behaviour. These factors, the associated probability distributions attributable to these factors and the relations between them (logic functions), will form the basis for decision making when implementing DWPT as part of the wider UK electric vehicle charging infrastructure and hence support the ambition to electrify all road transport. The results will make a significant contribution to the emerging knowledge base on ERS and specifically DWPT. [ABSTRACT FROM AUTHOR]

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers' smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions. [ABSTRACT FROM AUTHOR]

In order to have the best possible chance of achieving 'decent work' and 'climate action' as laid forth in the UN Sustainable Development Goals, government and policy makers must pay close attention to current time-use patterns, as well as the way these might change in the near future. Here we contribute to the existing literature on time-use behaviour through a systematic exploration of the relationship between working patterns and energy consumption from the perspective of time-use. Our starting point is the premise that different work arrangements impact the timing of energy demand not only in workplaces, but also at home. Using the data from the 2014–2015 UK time-use survey, we were able to capture patterns of time-use behaviours and to assess their relationship with daily energy consumption. We propose a systematic time-use-based approach for estimating residential energy consumption with regards to activity timing, activity location, activity coordination, and appliance type. We use this method to discover patterns in residential activities and energy consumption, as well as the causal relationship between residential energy consumption and work patterns. In this study, we unpack the heterogeneity in the work–energy relationship, particularly when comparing full-time and part-time workers. Our results suggest that full-time employees have a higher potential to reduce their energy use compared to part-time employees. We also discover a non-linear change in total energy consumption for respondents with varying levels of work time. Energy consumption reductions associated with differences in work schedules are greatest during the first few hours of the workday, but then level off. Our findings suggests that time-use data can provide useful insights for evaluating and possibly designing energy and labour-market policies. [ABSTRACT FROM AUTHOR]

Complex relations exist between issues of poverty, responsibility and just transitions toward reduced household energy use. One proposed transitional instrument is Personal Carbon Accounts (PCAs) which provide equal per capita carbon allowances and increase costs for additional usage. Previously modelled PCAs show that a third of households in poverty must curtail usage or pay more for some of their fuel, hitherto making PCAs ethically and politically untenable. Using the UK's "Understanding Society" database, average per capita carbon allowances and—using occupancy data—the hypothetical allowance each household would receive within a PCA scheme are calculated. Occupancy levels, equivalised incomes and conversion of expenditure to carbon emissions permit analysis of households emitting more or less carbon compared to their allocation. We demonstrate that households emitting greater than average levels of CO2 do so mainly for lifestyle reasons, irrespective of income. Any calculation of legitimate social and environmental cost of CO2, even for households in poverty, must consider questions of choice and capacity to act. This suggests that even if certain low income, high emitting households are disadvantaged by the transition associated with personal carbon allowances this may still be a just transition. [ABSTRACT FROM AUTHOR]

Energy usage in buildings is coming increasingly under the spotlight as carbon policy focus shifts towards the utilization of thermal energy. In the UK, heating and hot water accounts for around 40% of energy consumption and 20% of greenhouse gas emissions. Heating is typically produced onsite, making widescale carbon or energetic improvements challenging. District heating networks (DHNs) can offer significant carbon reduction for many users but can only be implemented if the end user buildings have good thermal energy efficiency. This greatly limits the ability to implement advancing 4th and 5th generation DHNs, which are the most advanced systems available. We elucidate the current state of thermal efficiency in buildings in the UK and provide recommendations for necessary building requirements and modifications in order to accommodate 4th and 5th generation district heating. We conclude that key sectors must be addressed including creating a skilled workforce, producing relevant metrics and benchmarks, and providing financial support for early stage design exploration. [ABSTRACT FROM AUTHOR]