Constructing load profiles for household electricity and hot water from time-use data—Modelling approach and validation

Time-use data, describing in detail the everyday life of household members as high-resolved activity sequences, have a largely unrealized potential of contributing to domestic energy demand modelling. A model for computation of daily electricity and hot-water demand profiles from time-use data was developed, using simple conversion schemes, mean appliance and water-tap data and general daylight availability distributions. Validation against detailed, end-use specific electricity measurements in a small sample of households reveals that the model for household electricity reproduces hourly load patterns with preservation of important qualitative features. The output from the model, when applied to a large data set of time use in Sweden, also shows correspondence to aggregate profiles for both household electricity and hot water from recent Swedish measurement surveys. Deviations on individual household level are predominantly due to occasionally ill-reported time-use data and on aggregate population level due to slightly non-representative samples. Future uses and developments are identified and it is suggested that modelling energy use from time-use data could be an alternative, or a complement, to energy demand measurements in households.     

A cross analysis of existing methods for modelling household appliance use

This paper presents a cross-analysis of the existing methods for modelling the use of household appliances and aims to provide insights into modelling approaches for researchers and designers. Five factors regarding appliance use modelling that have a significant impact on the modelling performance are defined: consideration of the intra/inter-household variation, consideration of the influence of socio-demographic conditions, time resolution of the data, quantification of model calibration parameters and applicability to a variety of modelling contexts. Four existing modelling methods commonly used in literature for modelling appliance use are studied to address these factors. Monitored data of 333 multi-family buildings in Japan and a Japanese time use survey are used in the cross-analysis to simulate the switch-on time profiles for the case of washing machines. The design of future research studies (including monitoring strategies, modelling and sample sizes) are discussed to further improve the ability to model home appliance use.     

Risk‐based water resources planning in practice: a blueprint for the water industry in England

Resilient water supplies in England need to be secured in the face of challenges of population growth, climate change and environmental sustainability. We propose a blueprint for water resources planning that uses system simulation modelling to estimate the frequency, duration and severity of water shortages at present and in the context of future plans and scenarios. We use multiobjective optimisation tools to explore trade‐offs between these risk metrics and cost of alternative plans, and we use sensitivity analysis to identify plans that robustly achieve targets for tolerable risk, alongside other performance objectives. The results of a case study in the Thames basin demonstrate that the proposed methodology is feasible given commonly available data sets and models. The proposed method provides evidence with which to develop water resource management plans that demonstrably balance the risks of water shortages, costs to water users and environmental constraints in an uncertain future.     

A critical review of methods used to obtain flow patterns and volumes of individual domestic water using appliances

Determining the daily flow rates and water use patterns of specific household fixtures and appliances is important for water use monitoring, deriving effective strategies for water demand management, designing onsite water reuse systems, and efficient planning and investment in the urban water sector. Currently, there are a limited number of publications which address the need to measure flow patterns and volumes of individual household water-using appliances, or which analyze the alternatives to do so. This paper critically reviews and compares different methods of obtaining the specific flow patterns and volumes of water used by individual household water-using microcomponents (i.e. toilet, bath/shower, washbasin, washing machine, kitchen sink, dishwasher, etc.) in order to make recommendations about the appropriateness of these methodologies for various applications.     

Efficiency assessment of household water use

This paper presents a novel methodology for assessing the overall efficiency of indoor water uses in the household. The methodology comprises three main components of assessment: the evaluation based on efficient patterns, the comparison with peers grouped by clustering techniques and the performance of water use devices based on penalty functions. A water use index is calculated for individual households for each component; this index can be used to compare and to rank the performance of different households. The most representative results from a real life case study composed of 43 households are presented herein. The proposed methodology can support urban demand managers to identify low efficiencies, to set viable water efficiency targets, to calculate potential water savings and, subsequently, to implement water demand management strategies customized to less-efficient water use devices and to groups of consumers with specific socio-demographic characteristics.     

A high-resolution domestic building occupancy model for energy demand simulations

Energy use in the home is a major source of carbon emissions and is highly dependent on the activities of the residents. More specifically, the timing of energy use, particularly electricity, is highly dependent on the timing of the occupants’ activities. Thus, in order to model domestic demand profiles with high temporal resolution, for example, in the context of designing and assessing demand side management systems (including the time-shifting of demand), it is of great benefit to take account of residents’ behaviour in terms of when they are likely to be using household appliances, lighting and heating. This paper presents a thorough and detailed method for generating realistic occupancy data for UK households, based upon surveyed time-use data describing what people do and when. The approach presented generates statistical occupancy time-series data at a ten-minute resolution and takes account of differences between weekdays and weekends. The model also indicates the number of occupants that are active within a house at a given time, which is important for example in order to model the sharing of energy use (shared use of appliances, etc.) The data from the model can be used as input to any domestic energy model that uses occupancy time-series as a base variable, or any other application that requires detailed occupancy data. The model has been implemented in Excel and is available for free download.     

浅谈住宅小区中水回用

阐述了中水的概念以及住宅小区可选择的水源,以某住宅小区为例,介绍了中水回用流程及一体化设备工艺流程,对中水回用处理设备及技术特点作了总结,并对其进行了经济分析,从而证明了中水回用的可行性.     

Making an engine: performativities of building information standards

ABSTRACT

Digitization of buildings requires the systematic handling of a variety and volumes of data. It is a common vision to aim for coordinated homogenization of data structures, enabled by the classification of information. This paper studies the role of building information standards for classification in digitization of the building life cycle. It draws on science and technology studies of information technology standards, big data and building information modelling (BIM) in building research. The approach is based on performativity of standards for information systems, viewing performativity as potentially non-linear and multiple. The five-year design process of a large hospital in Denmark is examined as episodes of performance of the building information standard, particularly one recent standard, the Cuneco Classification System (CCS). The study shows that despite client demands, several building information standards are active over time: an ‘expansive’ design brief process adds user demands and uses room classification. Another process diminishes the design brief more than 50%, with several standards in use. The client’s facilities management system, which performs the structuring of data ‘backwards’ into the design process, makes architects and engineers (but not contractors) use CCS. This fragmentation of performing standards is denoted ‘multiple performativities’ and includes temporal and compartmental performativity.     

Implementing integrated and systems approaches to water quality management considering data uncertainty

The paper emphasises the need for a system analysis approach at all scales in a water system considering all elements, subsystems and their interactions. In order to support this process in developing countries, the paper presents best management practices of monitoring and regulation of water resources. Furthermore, it recommends the integration of the modelling, decision and information support tools with the corresponding monitoring practices, regulatory instruments and management activities in a closed-loop cycle. Estimation and implications of monitoring data uncertainties are illustrated by two case study examples based on water quality monitoring data in the Southern African region. One is related to compliance with regulatory instruments and the other to pollution load assessments. Implications of monitoring data characteristics and uncertainty are discussed. Considering the conditions in developing countries, characterised by shortage of resources and lack of data, the application of adaptive management and modelling strategies in the water resources management practice is recommended.     

A new plant-wide modelling methodology for WWTPs

This paper presents a new plant-wide modelling methodology for describing the dynamic behaviour of water and sludge lines in WWTPs. The methodology is based on selecting the set of process transformations needed for each specific WWTP to model all unit-process elements in the entire plant. This "transformation-based" approach, in comparison with the conventional "process-based" approach, does not require the development of specific transformers to interface the resulting unit-process models, facilitates the mass and charge continuity throughout the whole plant and is flexible enough to construct models tailored for each plant under study. As an illustrative example, a plant-wide model for a WWTP that includes carbon removal and anaerobic digestion has been constructed, and the main advantages of the proposed methodology for integrated modelling have been demonstrated. As a final consequence, this paper proposes a rewriting of the existing unit-process models according to the new standard transformation-based approach for integrated modelling purposes.     

An analytical model for solute mixing in surcharged manholes

A mathematical model has been developed to describe solute mixing in surcharged manholes using the submerged jet theory. The model has been applied for straight-through flow manholes and for manholes with a difference in the level of the inlet and outlet pipes. The model is applicable to dissolved substances, i.e. the model has not been validated for high sediment concentrations (above 1 g/l) and care should be taken in such cases. Simulation results from the new model are compared with laboratory measurements and are further compared to conventional modelling techniques currently available in commercial software specially developed for modelling the water quality in sewers. The results prove that the new model produces considerably better results compared to the traditional assumption of full mixing in a manhole. The new model compares well to the laboratory measurements and hence improves the accuracy of modelling soluble pollutant transport in sewers. The applicability of the new modelling approach is discussed and further studies are recommended.     

Domestic lighting: A high-resolution energy demand model

The use of electric lighting in the domestic sector depends mainly on the level of natural light coming in from outdoors, coupled with the activity of the household residents. This paper presents a detailed model of domestic lighting use that takes these two factors as its basic inputs. The operation of individual bulbs is represented within the model and is used to construct high-resolution lighting electricity demand profiles for individual dwellings. The model is computationally efficient and can easily provide data at 1-min resolution for large numbers of dwellings. As a primary input, the model uses a time-series representing the number of active occupants within a dwelling (people who are at home and awake). This allows it to represent the sharing of lighting between the occupants of a given dwelling and facilitates correlated linking to models of other energy use within the dwelling. Appropriate correlation between dwellings is achieved through the use of appropriate active occupancy data and outdoor ambient light data. An example implementation of the model in Microsoft Excel is available for free download.     

Towards quantification of uncertainty in predicting water quality failures in integrated catchment model studies

This paper describes the development and application of a method for estimating uncertainty in the prediction of sewer flow quantity and quality and how this may impact on the prediction of water quality failures in integrated catchment modelling (ICM) studies. The method is generic and readily adaptable for use with different flow quality prediction models that are used in ICM studies. Use is made of the elicitation concept, whereby expert knowledge combined with a limited amount of data are translated into probability distributions describing the level of uncertainty of various input and model variables. This type of approach can be used even if little or no site specific data is available. Integrated catchment modelling studies often use complex deterministic models. To apply the results of elicitation in a case study, a computational reduction method has been developed in order to determine levels of uncertainty in model outputs with a reasonably practical level of computational effort. This approach was applied to determine the level of uncertainty in the number of water quality failures predicted by an ICM study, due to uncertainty associated with input and model parameters of the urban drainage model component of the ICM. For a small case study catchment in the UK, it was shown that the predicted number of water quality failures in the receiving water could vary by around 45% of the number predicted without consideration of model uncertainty for dissolved oxygen and around 32% for unionised ammonia. It was concluded that the potential overall levels of uncertainty in the ICM outputs could be significant. Any solutions designed using modelling approaches that do not consider uncertainty associated with model input and model parameters may be significantly over-dimensioned or under-dimensioned. With changing external inputs, such as rainfall and river flows due to climate change, better accounting for uncertainty is required.     

Computational modelling to investigate the sampling of lead in drinking water

The monitoring of lead in drinking water is beset by difficulties relating to the inherent temporal variation of lead emissions at individual premises. Such difficulties are compounded by spatial variation when considering an entire water supply area (e.g., City or Town), which is necessary to determine compliance with regulatory standards and to judge the efficacy of corrective measures. A computational modelling system, that uses a Monte Carlo probabilistic framework for simulating lead emissions within a water supply area, has been successfully validated in a range of UK case studies and enabled corrective treatment measures to be optimised for a range of water types. This modelling system includes the simulation of a range of sampling methods, and has made it possible to undertake an exhaustive comparison between daily average lead emissions (DAC - which are equivalent to weekly average lead concentrations as a consequence of the modelling system used), random daytime sampling (RDT), 30 min stagnation sampling (30MS) and 6 h stagnation sampling (6HS). It is concluded that: (a) the stringency of UK and US compliance assessment methods for lead in drinking water is fairly similar for waters of reduced plumbosolvency, despite different sampling approaches; (b) RDT sampling is equivalent to random DAC for waters of moderate plumbosolvency; (c) RDT sampling is more stringent than random DAC for waters of low plumbosolvency; (d) all random sampling methods suffer from poor reproducibility, albeit less so for low plumbosolvency water; and (e) fixed point stagnation sampling may not be representative.     

Residential water demand analysis of a Low‐Income Rate Assistance Program in California,United States

This paper presents a statistical analysis of residential water demand in a Low‐Income Rate Assistance (LIRA) Program. A comparison of 10 years of monthly household level water use data (2002–2011) for LIRA versus non‐LIRA customers revealed significant differences in water use patterns in four of the five study cities. In addition, seasonal index (averages summer versus winter water use) were investigated for LIRA versus non‐LIRA. There was a statistically significant difference for some cities; however, the level of difference was not large enough to conclusively demonstrate an outstanding difference between the two groups. Finally, the peaking factor approach coupled with the statistical modelling of demand elaborated in this paper will provide a more realistic way of representing residential water demand variations in system performance assessment/evaluations.     

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.     

Estimating energy savings from behaviours using building performance simulations

Occupants’ behaviours are a major determinant of energy use in buildings. The related savings potential has been insufficiently exploited. Although research has addressed behavioural savings through real-world interventions and quantitative modelling approaches, it has not yet explored the full variety of household activities. This work proposes an integrative modelling approach of energy behaviours in the residential setting as a tool to estimate the behavioural impact of households on energy consumption. It uses building energy performance simulation (BEPS) tools to exploit the behavioural savings potential associated with usage and investment energy behaviours when using different energy services in daily household activities. Simulations have estimated significant behavioural savings potential associated with energy behaviours, which may be materialized if some forms of behaviour are induced. Investment behaviours have higher savings potential than usage behaviours, and the behavioural savings potential per energy service is proportional to the energy consumption breakdown. BEPS tools enable a quantitative estimate of the behavioural impact on energy consumption, but further improvements to these tools are needed to incorporate the complexity of behavioural dimensions. Estimating the behavioural savings potential is important for a more effective design of behaviour change interventions, which in turn will support more effective energy efficiency policies.     

Assessment of the integrated urban water quality model complexity through identifiability analysis

Urban sources of water pollution have often been cited as the primary cause of poor water quality in receiving water bodies (RWB), and recently many studies have been conducted to investigate both continuous sources, such as wastewater-treatment plant (WWTP) effluents, and intermittent sources, such as combined sewer overflows (CSOs). An urban drainage system must be considered jointly, i.e., by means of an integrated approach. However, although the benefits of an integrated approach have been widely demonstrated, several aspects have prevented its wide application, such as the scarcity of field data for not only the input and output variables but also parameters that govern intermediate stages of the system, which are useful for robust calibration. These factors, along with the high complexity level of the currently adopted approaches, introduce uncertainties in the modelling process that are not always identifiable. In this study, the identifiability analysis was applied to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two WWTPs and has a small river as the RWB. The system was simulated by employing an integrated model developed in previous studies. The main goal of the study was to assess the right number of parameters that can be estimated on the basis of data-source availability. A preliminary sensitivity analysis was undertaken to reduce the model parameters to the most sensitive ones. Subsequently, the identifiability analysis was carried out by progressively considering new data sources and assessing the added value provided by each of them. In the process, several identifiability methods were compared and some new techniques were proposed for reducing subjectivity of the analysis. The study showed the potential of the identifiability analysis for selecting the most relevant parameters in the model, thus allowing for model simplification, and in assessing the impact of data sources for model reliability, thus guiding the analyst in the design of future monitoring campaigns. Further, the analysis showed some critical points in integrated urban drainage modelling, such as the interaction between water quality processes on the catchment and in the sewer, that can prevent the identifiability of some of the related parameters.     

Assessing the applicability of groundwater monitoring data in the modelling of soil water retention characteristics

The present work focuses on an assessment of the applicability of groundwater table (GWT) measures in the modelling of soil water retention characteristics (SWRC) using artificial neural network (ANN) methods. Model development, testing, validation and verification were performed using data collected across two decades from soil profiles at full‐scale research objects located in Southwest Poland. A positive effect was observed between the initial GWT position data and the accuracy of soil water reserve estimation. On the other hand, no significant effects were observed following the implementation of GWT fluctuation data over the entire growing season. The ANN tests that used data of either soil water content or GWT position gave analogous results. This revealed that the easily obtained data (temperature, precipitation and GWT position) are the most accurate modelling parameters. These outcomes can be used to simplify modelling input data/parameters/variables in the practical implementation of the proposed SWRC modelling variants.