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Herbicide resistance has been studied extensively in agronomic crops across North America but is rarely examined in vegetables. It is widely assumed that the limited number of registered herbicides combined with the adoption of diverse weed management strategies in most vegetable crops effectively inhibits the development of resistance. It is difficult to determine whether resistance is truly less common in vegetable crops or whether the lack of reported cases is due to the lack of resources focused on detection. This review highlights incidences of resistance that are thought to have arisen within vegetable crops. It also includes situations in which herbicide-resistant weeds were likely selected for within agronomic crops but became a problem when vegetables were grown in sequence or in adjacent fields. Occurrence of herbicide resistance can have severe consequences for vegetable growers, and resistance management plans should be adopted to limit selection pressure. This review also highlights resistance management techniques that should slow the development and spread of herbicide resistance in vegetable crops.

Based on results from a field survey campaign conducted in Switzerand, we show that occupants' variations in clothing choices, which are relatively unconstrained, are best described by the daily mean outdoor temperature and that major clothing adjustments occur rarely during the day. We then develop an ordinal logistic model of the probability distribution of discretised clothing levels, which results in a concise and informative expression of occupants' clothing choices. Results from both cross-validation and independent verification suggest that this model formulation may be used with confidence. Furthermore, the form of the model is readily generalisable, given the requisite calibration data, to environments where dress codes are more specific. We also observe that, for these building occupants, the prevailing metabolic activity levels are mostly constant for the whole range of surveyed environmental conditions, as their activities are relatively constrained by the tasks in hand. Occupants may compensate for this constraint, however, through the consumption of cold and hot drinks, with corresponding impacts on metabolic heat production. Indeed, cold drink consumption was found to be highly correlated with indoor thermal conditions, whilst hot drink consumption is best described by a seasonal variable. These variables can be used for predictive purposes using binary logistic models

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Aspects of the pathology and reproductive biology of Arceuthobium americanum Nuttal ex Engelman were examined with respect to microclimate and as they related to the inoculum potential and population dynamics of this pathogen. Fruit survival was measured over time to maturity and in successive years. Overwinter fruit survival was high in both years, averaging 94.9% and 87.5% respectively. Rates of fruit mortality increased through the following spring and summer months in both years of the study. As a result, fruit survival approximated a type I survivorship curve. Aerial shoot loss accounted for the greatest proportion of fruit mortality, ranging from 0.43 to 0.66 in the first year of the study and 0.32 to 1.00 in the following year. Shoot loss showed a significant correlation with vapour pressure deficit (r=0.87 in 1992/93; r=0.92 in 1993/94). Individual fruit loss ranged from 0.21 to 0.46 and from 0.18 to 0.61 respectively, in 1992/93 and 1993/94. Individual fruit loss showed a significant correlations with vapour pressure deficit in 1992/93 (r=0.91). The infection process, including overwinter seed retention, germination, factors influencing seed mortality and successful establishment of an infection, was also examined with respect to microclimate... Tree and tissue age were tested for their impact on infection success by inoculating the 1, 2, and 3 year-old tissues of 3, 5, and 7 year-old trees, and the 1, 4, and 8 year-old tissues of 12, 17, and 22 year-old trees... Seed germination in the laboratory experiments negatively correlated with vapour pressure deficit (p=0.0007), ranging from 30% at 2.50 kPa to 90% at 0.6 kPa. Below 0.3 kPa and temperatures of 10C, 15C, 20C and 25C there was a decrease in the proportion of seeds that germinated in the 1992-1993 laboratory experiments. Between 0.3 kPa and 0.0 kPa, seed germination was apparently inhibited by molding. In the 1993-1994 laborarory experiments, molding did not occur, though low values below 0.2 kPa at 10

Aspects of the pathology and reproductive biology of Arceuthobium americanum Nuttal ex Engelman were examined with respect to microclimate and as they related to the inoculum potential and population dynamics of this pathogen. Fruit survival was measured over time to maturity and in successive years. Overwinter fruit survival was high in both years, averaging 94.9% and 87.5% respectively. Rates of fruit mortality increased through the following spring and summer months in both years of the study. As a result, fruit survival approximated a type I survivorship curve. Aerial shoot loss accounted for the greatest proportion of fruit mortality, ranging from 0.43 to 0.66 in the first year of the study and 0.32 to 1.00 in the following year. Shoot loss showed a significant correlation with vapour pressure deficit (r=0.87 in 1992/93; r=0.92 in 1993/94). Individual fruit loss ranged from 0.21 to 0.46 and from 0.18 to 0.61 respectively, in 1992/93 and 1993/94. Individual fruit loss showed a significant correlations with vapour pressure deficit in 1992/93 (r=0.91). The infection process, including overwinter seed retention, germination, factors influencing seed mortality and successful establishment of an infection, was also examined with respect to microclimate... Tree and tissue age were tested for their impact on infection success by inoculating the 1, 2, and 3 year-old tissues of 3, 5, and 7 year-old trees, and the 1, 4, and 8 year-old tissues of 12, 17, and 22 year-old trees... Seed germination in the laboratory experiments negatively correlated with vapour pressure deficit (p=0.0007), ranging from 30% at 2.50 kPa to 90% at 0.6 kPa. Below 0.3 kPa and temperatures of 10C, 15C, 20C and 25C there was a decrease in the proportion of seeds that germinated in the 1992-1993 laboratory experiments. Between 0.3 kPa and 0.0 kPa, seed germination was apparently inhibited by molding. In the 1993-1994 laborarory experiments, molding did not occur, though low values below 0.2 kPa at 10

Aspects of the pathology and reproductive biology of Arceuthobium americanum Nuttal ex Engelman were examined with respect to microclimate and as they related to the inoculum potential and population dynamics of this pathogen. Fruit survival was measured over time to maturity and in successive years. Overwinter fruit survival was high in both years, averaging 94.9% and 87.5% respectively. Rates of fruit mortality increased through the following spring and summer months in both years of the study. As a result, fruit survival approximated a type I survivorship curve. Aerial shoot loss accounted for the greatest proportion of fruit mortality, ranging from 0.43 to 0.66 in the first year of the study and 0.32 to 1.00 in the following year. Shoot loss showed a significant correlation with vapour pressure deficit (r=0.87 in 1992/93; r=0.92 in 1993/94). Individual fruit loss ranged from 0.21 to 0.46 and from 0.18 to 0.61 respectively, in 1992/93 and 1993/94. Individual fruit loss showed a significant correlations with vapour pressure deficit in 1992/93 (r=0.91). The infection process, including overwinter seed retention, germination, factors influencing seed mortality and successful establishment of an infection, was also examined with respect to microclimate... Tree and tissue age were tested for their impact on infection success by inoculating the 1, 2, and 3 year-old tissues of 3, 5, and 7 year-old trees, and the 1, 4, and 8 year-old tissues of 12, 17, and 22 year-old trees... Seed germination in the laboratory experiments negatively correlated with vapour pressure deficit (p=0.0007), ranging from 30% at 2.50 kPa to 90% at 0.6 kPa. Below 0.3 kPa and temperatures of 10C, 15C, 20C and 25C there was a decrease in the proportion of seeds that germinated in the 1992-1993 laboratory experiments. Between 0.3 kPa and 0.0 kPa, seed germination was apparently inhibited by molding. In the 1993-1994 laborarory experiments, molding did not occur, though low values below 0.2 kPa at 10

Aspects of the pathology and reproductive biology of Arceuthobium americanum Nuttal ex Engelman were examined with respect to microclimate and as they related to the inoculum potential and population dynamics of this pathogen. Fruit survival was measured over time to maturity and in successive years. Overwinter fruit survival was high in both years, averaging 94.9% and 87.5% respectively. Rates of fruit mortality increased through the following spring and summer months in both years of the study. As a result, fruit survival approximated a type I survivorship curve. Aerial shoot loss accounted for the greatest proportion of fruit mortality, ranging from 0.43 to 0.66 in the first year of the study and 0.32 to 1.00 in the following year. Shoot loss showed a significant correlation with vapour pressure deficit (r=0.87 in 1992/93; r=0.92 in 1993/94). Individual fruit loss ranged from 0.21 to 0.46 and from 0.18 to 0.61 respectively, in 1992/93 and 1993/94. Individual fruit loss showed a significant correlations with vapour pressure deficit in 1992/93 (r=0.91). The infection process, including overwinter seed retention, germination, factors influencing seed mortality and successful establishment of an infection, was also examined with respect to microclimate... Tree and tissue age were tested for their impact on infection success by inoculating the 1, 2, and 3 year-old tissues of 3, 5, and 7 year-old trees, and the 1, 4, and 8 year-old tissues of 12, 17, and 22 year-old trees... Seed germination in the laboratory experiments negatively correlated with vapour pressure deficit (p=0.0007), ranging from 30% at 2.50 kPa to 90% at 0.6 kPa. Below 0.3 kPa and temperatures of 10C, 15C, 20C and 25C there was a decrease in the proportion of seeds that germinated in the 1992-1993 laboratory experiments. Between 0.3 kPa and 0.0 kPa, seed germination was apparently inhibited by molding. In the 1993-1994 laborarory experiments, molding did not occur, though low values below 0.2 kPa at 10

Only a small number of the subjects who enter treatment For obesity maintain their target weight. Explanations for this include increased food efficiency and alterations in resting metabolic rate (RMR). The RMR has been shown to be reduced after weight reduction and it is believed that a benefit of exercise training is an adaptive increase in lean body weight (LBW) with a concurrent decrease in excess body fat. Since caloric restriction is essential for weight reduction, an increase or preservation of LBW is desirable. The purpose of this study was to assess the effect of caloric restriction (DO group; n = 6) and caloric restriction plus weight training (DWT group; n = 8} on total weight (TW), percent fat (% fat), lean body weight (LBW), and RMR during an 8 week study. Both groups met 3 days/week for 30-45 min/day to perform a specific exercise or flexibility routine. The DWT group performed three sets of ten lifts on seven resistive exercises, under supervision, while the DO group performed a set routine designed to increase flexibility but result in no appreciable exercise. Subjects met weekly with a Registered Dietitian to provide information regarding nutritional and caloric content of Food, as well as behavior modification techniques. Results revealed non-Significant treatment effects between the groups on the variables RMR, % fat, and LBW. A treatment effect was observed for TW, with the DO group significantly losing more weight. Total weight and % fat significantly decreased in both groups from pre to posttest. A significant difference was noted for the DWT group from 4 weeks - 8 weeks in RMR, although there was no effect from baseline - 8 weeks. Thus, resistive exercise when added to caloric restriction, increases strength, but results in Slower weight loss when compared to diet only subjects while resulting in similar losses in % fat and total weight.

In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone (2001) [20] before applying the super SBM model of Tone (2002) [21] for the DMUs to achieve their super-efficiency scores, our integration can obtain the efficiency scores of the inefficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an one-stage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches.

The micro-simulation of buildings’ energy performance at the urban scale en- tails the dynamic simulation of buildings’ energy demands in a spatially resolved way; and of the supply of energy to meet these demands. Two approaches have emerged to meet the increasingly important need for such simulations: repeated calls to detailed dynamic simulation software that was designed for simulating single buildings, or a single call to simplified dynamic simulation software that was designed for simulating arbitrary numbers of buildings simultaneously. In this paper we describe a workflow that is under development to support application of the latter approach at multiple scales, ranging from single buildings, through neighborhoods, to districts and whole cities. This approach is based on CitySim, which remains to our knowledge the most comprehensive dedicated urban energy micro-simulation software. This workflow focuses on the utilisation by CitySim of an emerging standard for the semantic attribution of 3D urban models called CityGML, the import and export of CityGML files from a 3D database called 3DCityDB, methods for the population of this database, and on the generalisation of CitySim to solve for scenes of different spatial extent and level of detail. This paper describes this new workflow and its potential application to the simulation of urban scenes of varying complexity, from a simple street canyon to an urban district. This workflow is to represents our first step in the development of an integrated urban modelling platform for the simulation of physical resource flows and the socioeconomic phenomena that influence them. We close this paper by discussing the challenges that await us in the development of such a comprehensive urban modelling platform; challenges that range from the preparation of data, interoperability through the exchange of data for scenes of contrasting scale, the orchestration of services utilising this data and the visualisation of resultant o

Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub). This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epoch

This paper introduces a new general platform for the simulation of occupants' presence and behaviours. Called No-MASS (Nottingham Multi-Agent Stochastic Simulation platform) the platform takes a selection of well validated stochastic models to generate a synthetic population of agents, predicts their presence and, in the case of residences also their activities and inferred locations, as well as their use of windows, lights and blinds. A social interaction framework is used to emulate negotiations amongst the members of diverse populations. Furthermore, machine learning techniques allow the agents to learn dynamic behaviours that maximise energy and/ or comfort rewards. This is complemented by a belief-desire-intent framework for the representation of less sophisticated behaviours for which data is scarce. Using the Functional Mockup Interface (FMI) co-simulation standard No-MASS is coupled with EnergyPlus: EnergyPlus parses environmental parameters to No-MASS which in turns parses back the energetic consequences of agents behaviours. Simulations demonstrating the range of results that No-MASS can produce are undertaken and presented.

We have developed a spatiotemporal heating control algorithm for use in homes. This system utilises a combination of relatively low-tech hardware interfaced with electric heating systems and a smartphone interface to this hardware, and a central server that progressively learns users’ room-specific presence profiles and thermal preferences. This paper describes the associated spatiotemporal heating control algorithm, its evaluation utilising the dynamic building performance simulation software EnergyPlus, and a longitudinal deployment of the algorithm controlling a quasi-autonomous spatiotemporal home heating system in three domestic homes. In this we focus on the prediction of occupants’ presence and preferred set-point temperature as well as on the calculation of optimum start time and the utilisation of user-scheduled absences; this for two comfort strategies: to maximise comfort and to minimise discomfort. The former aims to deliver conditions equating to a ‘neutral’ thermal sensation, whereas the latter targets a ‘slightly cool’ sensation with corresponding heating energy savings. Simulation results confirmed that the algorithm functions as intended and that it is capable of reducing energy demand by a factor of seven compared with EnergyStar recommended settings for programmable thermostats. Field study results align with these findings and highlight the possibility to reduce energy under the minimise discomfort strategy without compromising on occupants’ thermal comfort.

This paper presents part of the outcomes of a programme of research into the influence of the thermal environment on human behaviour in an outdoor public seating area. The research was conducted during one month in summer, autumn and winter of 2015 and 2016. The data gathered consists in the conduct of people using a public square in Nottingham city centre, and measurements of the environmental conditions taken at that place. The data of Number of People and the Size of Groups of people, were analysed according with the thermal environment of the place. The results showed a strong significant correlation between Number of People and Globe Temperature_sun [r = .66, p < .001]. A multiple regression analysis found that the Number of People per minute in a public space can be predicted using the Globe Temperature_sun and the Wind Speed data of that place [R-square of .39, p < 0.001]. These prediction models can be used to forecast the occupancy of the place and the grouping of users under different environmental conditions. The results can assist the design of urban spaces by allowing testing their future use with predicted data of human behaviour. In addition, the data obtained will serve as a foundation for further research about the human behaviour in public spaces.

Rising energy demands place pressure on domestic energy consumption, but savings can be delivered through home automation and engaging users with their heating and energy behaviours. The aim of this paper is to explore user experiences (UX) of living with an automated heating system regarding experiences of control, understanding of the system, emerging thermal behaviours, and interactions with the system as this area is not sufficiently researched in the existing homes setting through extended deployment. We present a longitudinal deployment of a quasi-autonomous spatiotemporal home heating system in three homes. Users were provided with a smartphone control application linked to a self-learning heating algorithm. Rich qualitative and quantitative data presented here enabled a holistic exploration of UX. The paper's contribution focuses on highlighting key aspects of UX living with an automated heating systems including (i) adoption of the control interface into the social context, (ii) how users' vigilance in maintaining preferred conditions prevailed as a better indicator of system over-ride than gross deviation from thermal comfort, (iii) limited but motivated proactivity in system-initiated communications as best strategy for soliciting user feedback when inference fails, and (iv) two main motivations for interacting with the interface – managing irregularities when absent from the house and maintaining immediate comfort, latter compromising of a checking behaviour that can transit to a system state alteration behaviour depending on mismatches. We conclude by highlighting the complex socio-technical context in which thermal decisions are made in a situated action manner, and by calling for a more holistic, UX-focused approach in the design of automated home systems involving user experiences.

The UK housing stock is responsible for some 27% of national energy demand and associated carbon dioxide emissions. 80% of this energy demand is due to heating (60%) and domestic hot water (20%), the former reflecting the poor average thermal integrity of the envelope of the homes comprising this stock. To support the formulation of policies and strategies to decarbonise the UK housing stock, a large number of increasingly sophisticated Housing Stock Energy Models (HSEMs) have been developed throughout the past 25 years. After describing the sources of data and the spatio-temporal granularity with which these data are available to represent this stock, as well as the physical and social phenomena that are modelled and the range of strategies employed to do so, this paper evaluates the 29 HSEMs that have been developed and deployed in the UK. In this we consider the models' predictive accuracy, predictive sensitivity to design parameters, versatility, computational efficiency, the reproducibility of predictions and software usability as well as the models' transparency (how open they are) and modularity. We also discuss their comprehensiveness. From this evaluation, we conclude that current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and in the modelling of household behaviours relating to investment decisions and energy using practices. There is a need for an open-source and modular dynamic housing stock energy modelling platform that addresses current limitations, can be readily updated as new (e.g. housing survey) calibration data is released and be readily extended by the modelling community at large: improving upon the utilisation of scarce developmental resources. This would represent a considerable step forward in the formulation of housing stock decarbonisation policy that is informed by sound evidence.

Accurate forecasting of residential energy loads is highly influenced by the use of electrical appliances, which not only affect electrical energy use but also internal heat gains, which in turn affects thermal energy use. It is therefore important to accurately understand the characteristics of appliance use and to embed this understanding into predictive models to support load forecast and building design decisions. Bottom-up techniques that account for the variability in socio-demographic characteristics of the occupants and their behaviour patterns constitute a powerful tool to this end, and are potentially able to inform the design of Demand Side Management strategies in homes. To this end, this paper presents a comparison of alternative strategies to stochastically model the temporal energy use of low-load appliances (meaning those whose annual energy share is individually small but significant when considered as a group). In particular, discrete-time Markov processes and survival analysis have been explored. Rigorous mathematical procedures, including cluster analysis, have been employed to identify a parsimonious strategy for the modelling of variations in energy demand over time of the four principle categories of small appliances: audio-visual, computing, kitchen and other small appliances. From this it is concluded that a model of the duration for which appliances survive in discrete states expressed as bins in fraction of maximum power demand performs best. This general solution may be integrated with relative ease with dynamic simulation programs, to complement existing models of relatively large load appliances for the comprehensive simulation of household appliance use.

Understanding the use of electrical appliances in households is crucial for improving the accuracy of electricity and energy loads forecasts. In particular, bottom-up techniques provide a powerful tool, not only for predicting demands considering socio-demographic characteristics of the occupants, but also to better resolve and implement demand side management strategies in homes. With this purpose, a study of the temporal energy use of low-load appliances (meaning those whose annual energy share is individually negligible but relevant when considered as a group) has been carried out, with the longer term objective of finding a parsimonious approach to modelling them, and which considers an appropriate aggregation of appliances. In this work, a discrete-time stochastic process has been implemented for a specific classification of low-load appliances. More precisely, a time-inhomogeneous Markov chain has been used to model energy variations over time for four different categories of appliances and its prediction capabilities have been tested and compared.

In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone (2001) [20] before applying the super SBM model of Tone (2002) [21] for the DMUs to achieve their super-efficiency scores, our integration can obtain the efficiency scores of the inefficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an one-stage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches.

Sustainable municipal solid waste (MSW) management is regarded as one of the key elements for achieving urban sustainability via mitigating global climate change, recycling resources and recovering energy. Landfill is considered as the least preferable disposal method and the EU Landfill Directive (ELD) announced in 1999 requires member countries to reduce the volume of landfilled biodegradable materials. The enforcement of ELD initiated the evolution of MSW management system UK. This study depicted and assessed the transition and performance of MSW management after the millennium in Nottingham via materials flow analysis (MFA), as well as appropriately selected indicators based on the concept of waste management hierarchy and targets set in waste management regulations. We observed improvements in waste reduction, material recycling, energy recovery, and landfill prevention. During the period 2001/02 to 2016/17, annual waste generation reduced from 463 kg/Ca to 361 kg/Ca, the recycling and composting share increased from 4.6% to 44.4%, and the landfill share reduced from 54.7% to 7.3%. These signs of progress are believed to be driven by the ELD and the associated policies and waste management targets established at the national and local levels. An alternative scenario with food waste and textile separation at source and utilizing anaerobic digestion to treat separately collected organic waste is proposed at the end of this paper to fulfil the high targets set by local government and we further suggest that the recycling share may be improved by educating and supporting the public on waste separation at the sources.

The micro-simulation of buildings’ energy performance at the urban scale en- tails the dynamic simulation of buildings’ energy demands in a spatially resolved way; and of the supply of energy to meet these demands. Two approaches have emerged to meet the increasingly important need for such simulations: repeated calls to detailed dynamic simulation software that was designed for simulating single buildings, or a single call to simplified dynamic simulation software that was designed for simulating arbitrary numbers of buildings simultaneously. In this paper we describe a workflow that is under development to support application of the latter approach at multiple scales, ranging from single buildings, through neighborhoods, to districts and whole cities. This approach is based on CitySim, which remains to our knowledge the most comprehensive dedicated urban energy micro-simulation software. This workflow focuses on the utilisation by CitySim of an emerging standard for the semantic attribution of 3D urban models called CityGML, the import and export of CityGML files from a 3D database called 3DCityDB, methods for the population of this database, and on the generalisation of CitySim to solve for scenes of different spatial extent and level of detail. This paper describes this new workflow and its potential application to the simulation of urban scenes of varying complexity, from a simple street canyon to an urban district. This workflow is to represents our first step in the development of an integrated urban modelling platform for the simulation of physical resource flows and the socioeconomic phenomena that influence them. We close this paper by discussing the challenges that await us in the development of such a comprehensive urban modelling platform; challenges that range from the preparation of data, interoperability through the exchange of data for scenes of contrasting scale, the orchestration of services utilising this data and the visualisation of resultant o

In this paper, we develop an integrated model for slacks- based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone [20] before applying the super SBM model of Tone [21] for the DMUs to achieve their super-effciency scores, our integration can obtain the efficiency scores of the infficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an onestage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches

In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the efficiency and the super-efficiency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the efficient DMUs by the SBM model of Tone (2001) [20] before applying the super SBM model of Tone (2002) [21] for the DMUs to achieve their super-efficiency scores, our integration can obtain the efficiency scores of the inefficient DMUs and the super-efficiency scores of the efficient DMUs by solving simultaneously these two models by an one-stage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time effectiveness of our proposed model as compared with the traditional solution approaches.

Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub). This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epoch

We have developed a spatiotemporal heating control algorithm for use in homes. This system utilises a combination of relatively low-tech hardware interfaced with electric heating systems and a smartphone interface to this hardware, and a central server that progressively learns users’ room-specific presence profiles and thermal preferences. This paper describes the associated spatiotemporal heating control algorithm, its evaluation utilising the dynamic building performance simulation software EnergyPlus, and a longitudinal deployment of the algorithm controlling a quasi-autonomous spatiotemporal home heating system in three domestic homes. In this we focus on the prediction of occupants’ presence and preferred set-point temperature as well as on the calculation of optimum start time and the utilisation of user-scheduled absences; this for two comfort strategies: to maximise comfort and to minimise discomfort. The former aims to deliver conditions equating to a ‘neutral’ thermal sensation, whereas the latter targets a ‘slightly cool’ sensation with corresponding heating energy savings. Simulation results confirmed that the algorithm functions as intended and that it is capable of reducing energy demand by a factor of seven compared with EnergyStar recommended settings for programmable thermostats. Field study results align with these findings and highlight the possibility to reduce energy under the minimise discomfort strategy without compromising on occupants’ thermal comfort.

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this paper, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain (OSGB): MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment.

This paper introduces a new general platform for the simulation of occupants' presence and behaviours. Called No-MASS (Nottingham Multi-Agent Stochastic Simulation platform) the platform takes a selection of well validated stochastic models to generate a synthetic population of agents, predicts their presence and, in the case of residences also their activities and inferred locations, as well as their use of windows, lights and blinds. A social interaction framework is used to emulate negotiations amongst the members of diverse populations. Furthermore, machine learning techniques allow the agents to learn dynamic behaviours that maximise energy and/ or comfort rewards. This is complemented by a belief-desire-intent framework for the representation of less sophisticated behaviours for which data is scarce. Using the Functional Mockup Interface (FMI) co-simulation standard No-MASS is coupled with EnergyPlus: EnergyPlus parses environmental parameters to No-MASS which in turns parses back the energetic consequences of agents behaviours. Simulations demonstrating the range of results that No-MASS can produce are undertaken and presented.

As foci of economic activity, resource consumption, and the production of material waste and pollution, cities represent both a major hurdle and yet also a source of great potential for achieving the goal of sustainability. Motivated by the desire to better understand and measure sustainability in quantitative terms we explore the applicability of thermodynamic entropy to urban systems as a tool for evaluating sustainability. Having comprehensively reviewed the application of thermodynamic entropy to urban systems we argue that the role it can hope to play in characterising sustainability is limited. We show that thermodynamic entropy may be considered as a measure of energy efficiency, but must be complimented by other indices to form part of a broader measure of urban sustainability.

A rapid urban de-carbonization scenario analysis tool has been developed. The tool is able to efficiently and effectively generate and populate spatially resolved large scale building scenes, to generate XML input files for the building energy simulation engine CitySim [1], to quickly modify building thermal attributes and develop and analyze de-carbonization scenarios as snapshot modifications to the building scene. The tool has been developed as a series of plugins to the Quantum Geographical Information System (QGIS) [2] application, whereby it can make use of much of the QGIS existing functionality and software libraries. A tip to tail test of the tool is performed on a basic scenario.

Rising energy demands place pressure on domestic energy consumption, but savings can be delivered through home automation and engaging users with their heating and energy behaviours. The aim of this paper is to explore user experiences (UX) of living with an automated heating system regarding experiences of control, understanding of the system, emerging thermal behaviours, and interactions with the system as this area is not sufficiently researched in the existing homes setting through extended deployment. We present a longitudinal deployment of a quasi-autonomous spatiotemporal home heating system in three homes. Users were provided with a smartphone control application linked to a self-learning heating algorithm. Rich qualitative and quantitative data presented here enabled a holistic exploration of UX. The paper's contribution focuses on highlighting key aspects of UX living with an automated heating systems including (i) adoption of the control interface into the social context, (ii) how users' vigilance in maintaining preferred conditions prevailed as a better indicator of system over-ride than gross deviation from thermal comfort, (iii) limited but motivated proactivity in system-initiated communications as best strategy for soliciting user feedback when inference fails, and (iv) two main motivations for interacting with the interface – managing irregularities when absent from the house and maintaining immediate comfort, latter compromising of a checking behaviour that can transit to a system state alteration behaviour depending on mismatches. We conclude by highlighting the complex socio-technical context in which thermal decisions are made in a situated action manner, and by calling for a more holistic, UX-focused approach in the design of automated home systems involving user experiences.

This paper presents part of the outcomes of a programme of research into the influence of the thermal environment on human behaviour in an outdoor public seating area. The research was conducted during one month in summer, autumn and winter of 2015 and 2016. The data gathered consists in the conduct of people using a public square in Nottingham city centre, and measurements of the environmental conditions taken at that place. The data of Number of People and the Size of Groups of people, were analysed according with the thermal environment of the place. The results showed a strong significant correlation between Number of People and Globe Temperature_sun [r = .66, p < .001]. A multiple regression analysis found that the Number of People per minute in a public space can be predicted using the Globe Temperature_sun and the Wind Speed data of that place [R-square of .39, p < 0.001]. These prediction models can be used to forecast the occupancy of the place and the grouping of users under different environmental conditions. The results can assist the design of urban spaces by allowing testing their future use with predicted data of human behaviour. In addition, the data obtained will serve as a foundation for further research about the human behaviour in public spaces.

The UK housing stock is responsible for some 27% of national energy demand and associated carbon dioxide emissions. 80% of this energy demand is due to heating (60%) and domestic hot water (20%), the former reflecting the poor average thermal integrity of the envelope of the homes comprising this stock. To support the formulation of policies and strategies to decarbonise the UK housing stock, a large number of increasingly sophisticated Housing Stock Energy Models (HSEMs) have been developed throughout the past 25 years. After describing the sources of data and the spatio-temporal granularity with which these data are available to represent this stock, as well as the physical and social phenomena that are modelled and the range of strategies employed to do so, this paper evaluates the 29 HSEMs that have been developed and deployed in the UK. In this we consider the models' predictive accuracy, predictive sensitivity to design parameters, versatility, computational efficiency, the reproducibility of predictions and software usability as well as the models' transparency (how open they are) and modularity. We also discuss their comprehensiveness. From this evaluation, we conclude that current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and in the modelling of household behaviours relating to investment decisions and energy using practices. There is a need for an open-source and modular dynamic housing stock energy modelling platform that addresses current limitations, can be readily updated as new (e.g. housing survey) calibration data is released and be readily extended by the modelling community at large: improving upon the utilisation of scarce developmental resources. This would represent a considerable step forward in the formulation of housing stock decarbonisation policy that is informed by sound evidence.

Accurate forecasting of residential energy loads is highly influenced by the use of electrical appliances, which not only affect electrical energy use but also internal heat gains, which in turn affects thermal energy use. It is therefore important to accurately understand the characteristics of appliance use and to embed this understanding into predictive models to support load forecast and building design decisions. Bottom-up techniques that account for the variability in socio-demographic characteristics of the occupants and their behaviour patterns constitute a powerful tool to this end, and are potentially able to inform the design of Demand Side Management strategies in homes. To this end, this paper presents a comparison of alternative strategies to stochastically model the temporal energy use of low-load appliances (meaning those whose annual energy share is individually small but significant when considered as a group). In particular, discrete-time Markov processes and survival analysis have been explored. Rigorous mathematical procedures, including cluster analysis, have been employed to identify a parsimonious strategy for the modelling of variations in energy demand over time of the four principle categories of small appliances: audio-visual, computing, kitchen and other small appliances. From this it is concluded that a model of the duration for which appliances survive in discrete states expressed as bins in fraction of maximum power demand performs best. This general solution may be integrated with relative ease with dynamic simulation programs, to complement existing models of relatively large load appliances for the comprehensive simulation of household appliance use.

Understanding the use of electrical appliances in households is crucial for improving the accuracy of electricity and energy loads forecasts. In particular, bottom-up techniques provide a powerful tool, not only for predicting demands considering socio-demographic characteristics of the occupants, but also to better resolve and implement demand side management strategies in homes. With this purpose, a study of the temporal energy use of low-load appliances (meaning those whose annual energy share is individually negligible but relevant when considered as a group) has been carried out, with the longer term objective of finding a parsimonious approach to modelling them, and which considers an appropriate aggregation of appliances. In this work, a discrete-time stochastic process has been implemented for a specific classification of low-load appliances. More precisely, a time-inhomogeneous Markov chain has been used to model energy variations over time for four different categories of appliances and its prediction capabilities have been tested and compared.

Sustainable municipal solid waste (MSW) management is regarded as one of the key elements for achieving urban sustainability via mitigating global climate change, recycling resources and recovering energy. Landfill is considered as the least preferable disposal method and the EU Landfill Directive (ELD) announced in 1999 requires member countries to reduce the volume of landfilled biodegradable materials. The enforcement of ELD initiated the evolution of MSW management system UK. This study depicted and assessed the transition and performance of MSW management after the millennium in Nottingham via materials flow analysis (MFA), as well as appropriately selected indicators based on the concept of waste management hierarchy and targets set in waste management regulations. We observed improvements in waste reduction, material recycling, energy recovery, and landfill prevention. During the period 2001/02 to 2016/17, annual waste generation reduced from 463 kg/Ca to 361 kg/Ca, the recycling and composting share increased from 4.6% to 44.4%, and the landfill share reduced from 54.7% to 7.3%. These signs of progress are believed to be driven by the ELD and the associated policies and waste management targets established at the national and local levels. An alternative scenario with food waste and textile separation at source and utilizing anaerobic digestion to treat separately collected organic waste is proposed at the end of this paper to fulfil the high targets set by local government and we further suggest that the recycling share may be improved by educating and supporting the public on waste separation at the sources.