Dissertations / Theses on the topic 'Residential demand modelling'

Water supply-demand balances are becoming increasingly constrained around the world and in the United Kingdom. Although there has been a policy shift toward demand management policies to address this, demand modefling evidence is limited. This thesis makes qualitative, quantitative and behavioural contributions to this area. Qualitative and quantitative (meta-analytic) literature reviews are conducted. These indicate elasticity estimates are sensitive to methodological choices. Empiricafly it is identified that summer and long-run demand are more price and income responsive than their respective counterparts; lower income groups in developed countries are more price and less income responsive than higher income groups; and geographical demand differences exist. Publication bias tests reject bias, confirming that water is an economic good (price and income exert genuine effects on demand). A behavioural investigation of water consumers' price and consumption perceptions finds that systematic misperceptions of unit prices, consumption and tariff structures exist, regardless of which tariff structure operates. In contrast, bill perceptions are relatively accurate. This motivates a 'bill price' specification in addition to existing (marginal and average) price specifications. Perception inaccuracy is empiricafly tested against a simple explanatory framework of the costs and benefits of information acquisition. Price perceptions, but not consumption perceptions, broadly support this framework. Lastly, the first available price and wealth elasticity estimates for UK households are presented. These are -0.29 for price and +0.16 for wealth. These elasticities are generally smafler in magnitude than mean international price (-0.38) and income (+0.28) elasticities. Average and 'bill price' elasticities are significantly larger at around -0.S7. UK seasonal and income group differences appear to operate in the opposite direction to international trends (summer demand and lower income groups are less price responsive than their counterparts). Long-run UK demand appears more price responsive than the short-run. However, further research is required to develop consistent dynamic demand models in the presence of multiple endogenous variables.

Thesis (MScEng)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Energy efficiency and the move to renewable energy resources are of vital importance in growing profitable and sustainable economies. In recent years, greater emphasis has been placed on institutions, companies and individuals to reduce their electrical energy demand through energy management. In an attempt to reduce the demand, the electrical power utility in South Africa, Eskom, has introduced Demand Side Management programs and substantial increases in electricity tariffs. In addition to these, tax incentives have been offered to help off-set the capital costs associated with the investments made in replacing old electrical equipment with new electrically efficient equipment. Thus the need for accurate Measurement and Verification of electrical energy demand reduction, to substantiate fiscal claims, has become imperative. The main purpose of Measurement and Verification is to investigate the actual monetary performance of an energy savings project. Energy savings assessments, based on purely deterministic baseline demand, do not adequately represent the statistical nature of the savings impacts of many practical load systems, as disclosed in a reporting period. This thesis presents the development of a generic probabilistic methodology to determine the demand profiles of preand post-Energy Conservation Measures (ECMs) for practical load systems. The difference between the simulated demand of the pre- and post-ECMs for a particular set of variables represent the electrical demand impact. The electrical demand of the pre- and post-ECMs is defined in terms of Probability Density Functions, and derived using a multivariate kernel density estimation algorithm. The approach is tested using a simulation model of a waterheating geyser implemented in MATLAB. Three different ECMs are simulated using the geyser model and demand density estimation. The results of the demand impacts of the ECMs are presented and evaluated. With regards to possible future research this methodology could be applied to the evaluation of the demand impacts of heat pump technologies and solar water heaters.
AFRIKAANSE OPSOMMING: en die skuif na hernubare energiebronne is van deurslaggewende belang vir die ontwikkeling van winsgewende en volhoubare ekonomieë. Onlangs is meer klem geplaas op instansies, maatskappye en individue om hul aanvraag na energie te verminder met behulp van energiebestuur. In ‘n poging om die aanvraag te verlaag, het Eskom, Suid-Afrika se elektrisiteitsverskaffer, aansienlike elektrisiteitstariefverhogings ingelyf en Aanvraagbestuursprogramme van stapel gestuur. Bykomend hiertoe is belastingaansporings ook aangebied, waarteen kapitale kostes, geassosieer met die vervanging van ou elektriese toerusting met nuwe elektries doeltreffende toerusting, afgeset kan word. Derhalwe het die behoefte aan akkurate Meting en Verifikasie van elektriese energie aanvraagvermindering, om finansiële eise te staaf, noodsaaklik geword. Die hoofdoel van Meting en Verifikasie is om die werklike finansiële prestasie van energiebesparingsprojek te ondersoek soos bekend gemaak word tydens ’n verslagdoeningstydperk. Energiebesparingassesserings wat slegs gebaseer word op die suiwer deterministiese basislyn aanvraag na elektrisiteit, verteenwoordig nie die werklike statistiese aard van die besparingsimpakte van baie praktiese lasstelsels nie. Hierdie tesis stel die ontwikkeling van generiese waarskynlikheids-metodologie voor, om die voor- en na- Energiebesparings-maatreëls se aanvraagprofiele vir sulke praktiese lasstelsels, vas te stel. Die verskil tussen die gesimuleerde aanvraag van die voor- en na- Energiebesparings-maatreëls vir spesifieke stel veranderlikes verteenwoordig die elektriese aanvraag impak. Die voor- en na- Energiebesparings-maatreëls van die energieverbruik profieldata word gedefinieer in terme van Waarskynlikheidsdigtheidsfunksies en afgelei deur gebruik te maak van meerveranderlike kerndigtheidafskattingsalgoritme. Die benadering is getoets deur gebruik te maak van simuleringsmodel van warmwaterstelsel geïmplimenteer in MATLAB. Drie verskillende voor- en na- Energiebesparings-maatreëls is gesimuleer met behulp van die warmwaterstelselmodel en aanvraag digtheidafskatting. Die resultate van die elektriese aanvraag impakte van die voor- en na- Energiebesparings-maatreëls word vervolgens bespreek en geëvalueer. Met betrekking tot moontlike toekomstige navorsing kan hierdie metodologie toegepas word om die aanvraag impakte van hittepomp- en sonwaterverwarmingstegnologieë te evalueer.

Demand Response (DR) eliminates the need for expensive capital expenditure on the electricity distribution, transmission and the generation systems by encouraging consumers to alter their power usage through electricity pricing or incentive programs. However, modelling of DR programs for residential consumers is complicated due to the uncertain consumption behavious of consumers and the complexity of schedulling a large number of household appliances. This thesis has investigated the design and the implementation challenges of the two most commonly used DR components in the residential sector, i.e., time of use (TOU) and direct load control (DLC) programs for improving their effectiveness and implementation with innovative strategies to facilitate their acceptance by both consumers and utilities. In price-based DR programs, the TOU pricing scheme is one of the most attractive and simplest approaches for reducing peak electricity demand in the residential sector. This scheme has been adopted in many developed countries because it requires less communication infrastructure for its implementation. However, the implementation of TOU pricing in low and lower-middle income economies is less appealing, mainly due to a large number of low-income consumers, as traditional TOU pricing schemes may increase the cost of electricity for low income residential consumers and adversely affect their comfort levels. The research in this thesis proposes an alternative TOU pricing strategy for the residential sector in developing countries in order to manage peak demand problems while ensuring a low impact on consumers’ monthly energy bills and comfort levels. In this study, Bangladesh is used as an example of a lower-to-middle income developing country. The DLC program is becoming an increasingly attractive solution for utilities in developed countries due to advances in the construction of communication infrastructures as part of the smart grid concept deployment. One of the main challenges of the DLC program implementation is ensuring optimal control over a large number of different household appliances for managing both short and long intervals of voltage variation problems in distribution networks at both medium voltage (MV) and low voltage (LV) networks, while simultaneously enabling consumers to maintain their comfort levels. Another important challenge for DLC implementation is achieving a fair distribution of incentives among a large number of participating consumers. This thesis addresses these challenges by proposing a multi-layer load control algorithm which groups the household appliances based on the intervals of the voltage problems and coordinates with the reactive power from distributed generators (DGs) for the effective voltage management in MV networks. The proposed load controller takes into consideration the consumption preference of individual appliance, ensuring that the consumer’s comfort level is satisfied as well as fairly incentivising consumers based on their contributions in network voltage and power loss improvement. Another significant challenge with the existing DLC strategy as it applies to managing voltage in LV networks is that it does not take into account the network’s unbalance constraints in the load control algorithm. In LV distribution networks, voltage unbalance is prevalent and is one of the main power quality problems of concern. Unequal DR activation among the phases may cause excessive voltage unbalance in the network. In this thesis, a new load control algorithm is developed with the coordination of secondary on-load tap changer (OLTC) transformer for effective management of both voltage magnitude and unbalance in the LV networks. The proposed load control algorithm minimises the disturbance to consumers’ comfort levels by prioritising their consumption preferences. It motivates consumers to participate in DR program by providing flexibility to bid their participation prices dynamically in each DR event. The proposed DR programs are applicable for both developed and developing countries based on their available communication infrastructure for DR implementation. The main benefits of the proposed DR programs can be shared between consumers and their utilities. Consumers have flexibility in being able to prioritise their comfort levels and bid for their participation prices or receive fair incentives, while utilities effectively manage their network peak demand and power quality problems with minimum compensation costs. As a whole, consumers get the opportunity to minimise their electricity bills while utilities are able to defer or avoid the high cost of their investment in network reinforcements.

Peak demand is an issue in power supply system when demand exceeds the available capacity. Continuous growth in peak demand increases the risk of power failures, and increases the marginal cost of supply. The contribution of the residential sector to the system peak is quite substantial and has been a subject of discussion internationally. For example, a study done in New Zealand in 2007 attributed about half of system peak load to the residential sector. International research has attributed a significant influence of human behaviour on households energy use. “Demand Response” is a demand side management tool aimed at achieving peak energy demand reduction by eliciting behaviour change. It encompasses energy needs analysis, information provision to customers, behaviour induction, smart metering, and new signalling and feedback concepts. Demand response is far advanced in the industrial and commercial demand sectors. In the residential sector, information barriers and a lack of proper understanding of consumers’ behaviour have impeded the development of effective response strategies and new enabling technologies in the sector. To date, efforts to understanding residential sector behaviour for the purpose of peak demand analysis has been based on pricing mechanism. However, not much is known about the significance of other factors in influencing household customers’ peak electricity demand behaviour. There is a tremendous amount of data that can be analyzed and fed back to the user to influence behaviour. These may include information about energy shortages, supply security and environmental concerns during the peak hours. This research is intended to begin the process of understanding the importance of some of these factors in the arena of peak energy consumption behaviour. Using stated preference survey and focus group discussions, information about household customers’ energy use activities during winter morning and evening peak hours was collected. Data about how customers would modify their usage behaviour when they receive enhanced supply constraint information was also collected. The thesis further explores households’ customer demand response motivation with respect to three factors: cost (price), environment (CO2-intensity) and security (risk of black-outs). Householders were first informed about the relationship between these factors and peak demand. Their responses were analyzed as multi-mode motivation to energy use behaviour change. Overall, the findings suggest that, household customers would be willing to reduce their peak electricity demand when they are given clear and enhanced information. In terms of motivation to reduce demand the results show customers response to the security factor to be on par with the price factor. The Environmental factor also produced a strong response; nearly two-thirds of that of price or security. A generic modelling methodology was developed to estimate the impact of households’ activity demand response on the load curve of the utility using a combination of published literature reviews and resources, and own research work. This modelling methodology was applied in a case study in Halswell, a small neighbourhood in Christchurch, New Zealand, with approximately 400 households. The results show that a program to develop the necessary technology and provide credible information and understandable signals about risks and consequences of peak demand could provide up to about 13% voluntary demand reduction during the morning peak hours and 8% during the evening peak hours.

This research evaluates induced travel demand with the construction of transport infrastructure in Dhaka, Bangladesh using flyovers as a case study. It examines whether transport infrastructure induces travel behaviour changes of individuals and explains that flyover users did not generate any induced travel kilometres. However, flyover users switched their travel mode, route, and residential location for travel time savings. The findings contribute to guide policies that include the effects of induced travel demand when constructing new roadway facilities, such as flyovers in Dhaka and other similar cities in developing countries.

Building and urban energy simulation software aim to model the energy flows in buildings and urban communities in which most of them are located, providing tools that assist in the decision-making process to improve their initial and ongoing energy performance. To maintain their utility, they must continually develop in tandem with emerging technologies in the energy field. Demand Response (DR) strategies represent one such family of technology that has been identified as a key and affordable solution in the global transition towards clean energy generation and use, in particular at the residential scale. This thesis contributes towards the development and application of a comprehensive building and urban energy simulation capability that parsimoniously represents occupants' energy using behaviours and responses to strategies to influence them. This platform intends to better unify the modelling of Demand Response strategies, by integrating the modelling of different energy systems through Multi Agent Simulation, considering stochastic processes taking place in electricity demand and supply. This is addressed by: (a) improving the fidelity of predictions of household electricity demand, using stochastic models, (b) demonstrating the potential of Demand Response strategies using Multi-Agent Simulation and machine learning techniques, (c) integrating a suitable model for the low voltage network to study and incorporate effects on the grid, (d) identifying how this platform should be extended to better represent human-to-device interactions; to test strategies designed to influence the scope and timing of occupants' energy using services. Stochastic demand models provide the means to realistically simulate power demands, which are subject to naturally random human behaviour. In this work, the power demand arising from small household appliances is identified as a stochastic variable, for which different candidate modelling methods are explored. Variants of two types of stochastic models have been tested, based on discrete time and continuous time stochastic processes. The alternative candidate models are compared and validated using Household Electricity Survey data, which is also used to test strategies, informed by advanced cluster analysis techniques, to simplify the form of these models. The recommended small appliance model is integrated with a Multi Agent Simulation (MAS) platform, which is in turn extended and deployed to test DR strategies, such as load shifting and electric storage operation. In the search for optimal load-shifting strategies, machine learning algorithms, Q-learning in particular, are utilised. The application of this new developed tool, No-MASS/DR, is demonstrated through the study of strategies to maximise the locally generated renewable energy of a single household and a small community of buildings connected to a Low Voltage network. Finally, an explicit model of the Low Voltage (LV) network has been developed and coupled with the DR framework. The model solves for power-flow analysis of a general low-voltage distribution network, using an electrical circuit-based approach, implemented as a novel recursive algorithm, that can efficiently calculate the voltages at different nodes of a complex branched network. The work accomplished in this thesis contributes to the understanding of residential electricity management, by developing better unified modelling of Demand Response strategies, that require integrated modelling of energy systems, with a particular focus on the study of maximising locally generated renewable energy.

The Okanagan Basin, the most arid watershed in Canada per capita, has been undergoing rapid population growth in the past 30 years. The results of this research show the varying residential water demand in the major communities in the Okanagan Basin. The outdoor water use was determined on a lot size basis for 2006 and projections were made to the year 2026 using a projected population increase and three urban development scenarios. The outdoor residential water demand varied from 30% to 60% of the annual domestic water demand depending on lot size. Two extreme urban growth cases: urban sprawl and densification, and an in-between scenario were developed using GIS for four case study communities: Vernon, Kelowna, Penticton and Osoyoos, which represent the diversity in climate and population trends in the Basin. The results showed that currently 47% of the domestic water is used for outdoor watering for the case studies. The business as usual growth scenario (urban sprawl), includes climate change and results in 7.3 Mm3 of additional water needed for outdoor residential water demand, a 55% increase from existing conditions. However, with densification, 5.7 Mm3 of outdoor residential water savings can be made. Additional water savings can be made if the existing outdoor residential water demand is reduced through more aggressive conservation practices such as effective marketing, reduced grassed area through xeriscape and mandatory watering restrictions. Using current growth projection to 2026 it is possible to significantly reduce outdoor residential water demand by more than 50% from existing conditions if densification and aggressive conservation practices are put in place. The methods and results of this research have been incorporated into the Okanagan Basin Water Supply and Demand Project, which is in the final phases of completion.

Interest has been growing in the interaction of various power demand transformations, such as demand side management (DSM) and voltage control, with the power demand. Initial studies have highlighted the need for a better understanding of the power demand of low voltage (LV) residential networks. Furthermore, it is expected that future alteration of the residential appliance mixture, because of the advances in technology, will have an impact on both the demand curve as well as the electrical characteristics. This thesis presents a study of the impact of current and future household load on the power demand curve and the network operation. In order to achieve this, a bottom-up load modelling tool was developed to create LV detailed demand profiles that include not only the active and reactive power demand, but their electrical characteristics as well. The methodology uses a Markov chain Monte Carlo approach to generate residential LV demand profiles taking into account the user activity and behaviour to represent UK population. An appliance database has also been created which corresponds to the UK residential appliance mixture in order to calculate more accurately the power demand. The main advantages of the approach presented here are the flexibility in altering the type and number of the appliances that populate a household and how easily it can be adapted to a different population, location and climate. The tool is used to investigate the impact of scenarios that simulate future load replacement and the network behaviour under certain methods of demand control, implementation of DSM and control of voltage on the secondary of the LV transformer. The algorithm that was developed to apply the DSM actions on the power demand focused on the management of individual loads. The drivers used in this approach were the financial and environmental benefit of customers and the increase in the quality of the network operation. The control of the voltage as a method for power reduction takes into account the voltage dependence of the demand. The primary target is to quantify the benefits of this strategy either in combination with DSM for higher power reduction during the peak hours or on the current network as a quicker, easier and less expensive alternative to DSM. The study shows that there is a significant power reduction in both cases which is dependent on the time of day and not constant as expected from the literature. The results show that there are significant differences between current and future load demand characteristics that would be very difficult to acquire without the modelling technique presented. The alternative solution would require extensive local load and network modifications and a long period of expensive tests and measurements in the field.

The residential sector in England is often identified as having the largest potential for emissions reduction at some of the lowest costs when compared against other sectors. In spite of this, decarbonisation within the residential sector has not materialised. This thesis explores the complexities of decarbonising the residential sector in England using a whole systems approach. It is only when the interaction between social, psychological, regulatory, technical, material and economic factors are considered together that the behaviour of the system emerges and the relationships between different system components can be explained giving insight into the underlying issues of decarbonisation. Building regulations, assessments and certification standards are critical for motivating and driving innovation towards decarbonising the building stock. Many existing building performance and evaluation tools are shown to be ineffective and confound different policy objectives. Not only is the existing UK SAP standard shown to be a poor predictor of dwelling level energy demand but it perversely incentivises households to increase CO2 emissions. At the dwelling level, a structural equation model is developed to quantify direct, indirect and total effects on residential energy demand. Interestingly, building efficiency is shown to have reciprocal causality with a household’s propensity to consume energy. That is, dwellings with high-energy efficiency consume less energy, but homes with a propensity to consume more energy are also more likely to have higher energy efficiency. Internal dwelling temperature is one of the most important parameters for explaining residential energy demand over a heterogeneous building stock. Yet bottom up energy demand models inadequately incorporate internal temperature as a function of human behaviour. A panel model is developed to predict daily mean internal temperatures from individual dwellings. In this model, socio-demographic, behavioural, physical and environmental variables are combined to estimate the daily fluctuations of mean internal temperature demand. The internal temperature prediction model is then incorporated in a bottom-up engineering simulation model. The residential energy demand model is then used to project decarbonisation scenarios to 2050. Under the assumption of consistent energy demand fuel share allocation, modelling results suggest that emissions from the residential sector can be reduced from 125 MtCO2 to 44 MtCO2 after all major energy efficiency measures have been applied, the power sector is decarbonised and all newly constructed dwellings are zero carbon. Meeting future climate change targets will thus not only require extensive energy efficiency upgrades to all existing dwellings but also the complete decarbonisation of end use energy demand. Such a challenge can only be met through the transformation of existing building regulations, models that properly allow for the effects of human behaviour, and flexible policies capable of maximising impact from a heterogeneous residential building stock.

This thesis presents the development of a new, stochastic bottom-up model for predicting household appliance energy demand, named the Household Appliance Usage (HAU) model. Three sub models are developed which have different functions and are based on different supporting datasets. Firstly, 2013-2014 English Housing Survey (EHS), a UK Government national representative household sample of around 17,000 homes, is chosen to provide a platform to generate the electricity demand profiles. Secondly, an appliance ownership model is developed where the nationally representative household sample is populated with electrical appliances using the appliance saturation levels derived from 2011 UK Government s Energy Follow-Up Survey (EFUS) of 3000 homes. Thirdly, an occupant behaviour model is developed where appliance behaviour metrics are simulated using monitored data from the UK Government s 2011 Household Electricity Survey (HES) of 225 homes, and electricity demand profiles are generated using the results of the appliance behaviour model. A new approach to bottom-up occupant behaviour modelling for predicting the use of household electrical appliances in domestic buildings is presented. Stochastic model predictions are made for individual households and appliances which can be used as inputs for the dynamic thermal simulation of buildings. Three metrics relating to appliance occupant behaviours are defined: the number of switch-on events per day, the switch-on times, and the duration of each appliance usage. The metrics were calculated for 1,076 appliances in 225 households in the HES sample. The analysis shows that occupant behaviour varies substantially between households, across appliance types and over time. This new modelling approach uses probability and cumulative distribution functions to capture daily variations and is based on individual households and appliances. It is shown to have advantages for modelling the variations in appliance occupant behaviours. Two minutely household appliance electricity demand profiles are generated using the appliance behaviour metrics and power demand during usage. The comparison of simulation results and measured values show that the HAU model daily power demand predictions closely match the measured data (up to 8% difference during peak time). iii The final HAU (Household Appliance Usage) model, which generates aggregate electricity demand profiles of 13,276 households that were randomly populated with appliances for 16 appliance types, is scaled up to national level using the weighting factors calculated by the 2013-2014 EHS study. The HAU model is then applied to demand shifting of individual appliance in the households to evaluate the extent that electricity demand could be shaped by time shifting. The findings provide insights about the amount of residential load that is available for shifting and a discussion is presented on the future potential of household electricity demand response. The implications of findings for policy, industry and research are discussed. The thesis discusses the design of future monitoring studies (including monitoring strategies and sample sizes) and the design of future research studies (including statistical analysis, probabilistic modelling and validation approaches) in order to further improve our understanding of and ability to predict the behaviours of occupants and their use of household appliances within buildings.

Residential space and water heating accounts for around 13% of the greenhouse gas emissions of the UK. Reducing this is essential for meeting the national emission reduction target of 80% by 2050 from the 1990 baseline. One of the strategies adopted for achieving this is focused around large scale shift towards electrical heating. This could lead to unsustainable disparity between the daily peak and off-peak electricity loads, large seasonal variation in electricity demands, and challenges of matching the short and long term supply with the demands. These challenges could impact the security and resilience of UK electricity supply, and needs to be addressed. Rechargeable Thermal Energy Storage (TES) in residential buildings can help overcome these challenges by enabling Heat Demand Shifts (HDS) to off-peak times, reducing the magnitude of the peak loads, and the difference between the peak and off-peak loads. To be effective a wide scale uptake of TES would be needed. For this to happen, the benefits and impacts of TES both for the demand side and the supply side have to be explored, which could vary considerably given the diverse physical, thermal, operational and occupancy characteristics of the UK housing stock. A greater understanding of the potential consequence of TES in buildings is necessary. Such knowledge could enable appropriate policy development to help drive the uptake of TES or to encourage development of alternative solutions. Through dynamic building simulation in TRNSYS, this work generated predictions of the space and water heating energy and power demands, and indoor temperature characteristics of the UK housing stock. Twelve building archetypes were created consisting of: Detached, semi-detached, mid-terrace and flat built forms with thermal insulation corresponding to the 1990 building regulation, and occupied floor areas of 70m2, 90m2 and 150m2. Typical occupancy and operational conditions were used to create twelve Base Case scenarios, and simulations performed for 60 winter days from 2nd January. HDS of 2, 3 and 4 hours from the grid peak time of 17:00 were simulated with sensible TES system sizes of 0.25m3, 0.5m3 and 0.75m3, and water storage temperatures of 75°C and 95°C. Parametric analysis were performed to determine the impacts and benefits of: thermal insulation equivalent to 1980, 1990 (Base Case), 2002 and 2010 building regulation; locations of Gatwick (Base Case) and Aberdeen; heating durations of 6, 9 (Base Case), 12 and 16 hours per day; thermostat settings of 19°C, 21°C (Base Case) and 23°C, and number of occupiers of 1 person and 3 persons (Base Case) per household. Good correlation was observed between the simulated results and published heat energy consumption data for buildings with similar thermal, physical, occupancy and operational conditions. The results allowed occupied space temperatures and overall daily and grid peak time energy consumption to be predicted for the range of building archetypes and parameter values considered, and the TES size necessary for a desired HDS to be determined. The main conclusions drawn include: The overall daily energy consumption predictions varied from 36.8kWh to 159.7kWh. During the critical grid peak time (17:00 to 21:00) the heat consumption varied from 4.2kWh to 58.7kWh, indicating the range of energy demands which could be shifted to off-peak times. On average, semi-detached, mid-terrace, and flat built forms consumed 7.0%, 13.8% and 22.7% less energy for space heating than the detached built form respectively. Thermal insulation changing from the 1990 building regulation level to the 1980 and 2010 building regulation levels could change the mean energy use by +14.7% and -19.6% respectively. A 0.25m3 TES size with 75°C water storage temperature could enable a 2 hour HDS, shifting 4.3kWh to 11.7kWh (mean 8.7kWh) to off peak times, in all 70m2 Base Case archetypes with the 60 day mean thermal comfort of 100%, but with the minimum space temperature occasionally dropping below an 18°C thermal comfort limit. A 0.5m3 TES size and water storage of 95°C could allow a 3 hour HDS, shifting 9.8kWh to 28.2kWh (mean 18.7kWh) to off peak times, in all 90m2 Base Case archetypes without thermal comfort degradation below 18°C. A 0.75m3 TES with a 95°C water temperature could provide 4 hour HDS, shifting 13.9kWh to 47.7kWh (mean 27.2kWh) to off peak times, in all 150m2 Base Case archetypes with 100% mean thermal comfort but with the 60 day minimum temperature occasionally dropping below the 18°C thermal comfort limit in the detached built form. Improving the thermal insulation of the buildings was found to be the best way to improve the effectiveness of HDS with TES, in terms of the demand shift period achievable with minimal thermal comfort impact. A 4 hour HDS with 100% thermal comfort is possible in all 90m2 floor area buildings with a 0.25m3 tank and a water storage temperature of 75°C provided that the thermal insulation is as per 2010 building regulation. Recommendations for further research include: 1) creating larger number of archetype models to reflect the housing stock; 2) using heat pumps as the heat source so that the mean effect on the grid from electric heating loads can be predicted; 3) taking into account the costs associated with taking up HDS with TES, in terms of capital expenses and space requirement for housing the TES system; 4) considering alternative methods of heat storage such as latent heat storage to enhance the storage capacity per unit volume; and 5) incorporating zonal temperature control, for example, only heating rooms that are occupied during the demand shift period, which could ensure better thermal comfort in the occupied space and extend the demand shift period.

Les territoires ruraux disposent du principal gisement d’énergie renouvelable en France. Les réseaux énergétiques y sont moins denses que dans les zones urbaines et certains vecteurs, tels que le gaz, en sont souvent absents. Or, alors que les systèmes énergétiques urbains ont été abondamment étudiés, les spécificités de la demande énergétique rurale restent méconnues, notamment dans le secteur résidentiel. Des travaux récents mettent en avant les enjeux liés à la décentralisation du système énergétique français et le besoin d’une connaissance fine de l’offre et de la demande, tant sur le plan spatial que temporel. Ce travail de thèse poursuit deux objectifs. Tout d’abord il s’attache à identifier les spécificités de la consommation énergétique des logements ruraux par rapport aux logements urbains. Ensuite, il vise à analyser la réponse que peut apporter le gisement local d’énergie renouvelable à la demande résidentielle sur un territoire mixte urbain-rural, dans une optique de territoire à énergie positive – équilibre annuel entre l’offre et la demande énergétique du territoire
Rural areas have the main resources of renewable energy in France. Energy networks are less dense there than in urban areas and some energy vectors, like gas, are often missing. However, as urban energy systems have been widely studied, the specificities of rural energy demand remain little-known, especially for the residential sector. Recent works highlight new challenges related to decentralization of the French energy system and the need for fine knowledge of demand and supply, on both spatial and time scales. This research work pursues two objectives. First, it commits to identify the specificities of rural housing energy consumption. Then, it aims at analyzing the potential response of local renewable energy sources to the residential demand in a mixed urban-rural territory, in a 100 % RES process – equilibrium between annual energy demand and supply on the territory

With an increasing world population and changing lifestyles, there is a relentless demand for water including for domestic water supplies. In order to manage water demand better, the amount of water used for domestic purposes must be estimated. A number of methods exist such as the micro-components method, which is recommended by the UK Environment Agency. Microsimulation of synthetic households is also used for demand estimation, which is an area of research that has a tradition in the School of Geography, University of Leeds. This research project follows in this tradition but extends the work by adding a behavioural component through information collected via a survey. Microsimulation is used to create a synthetic household population for the city of Leeds, which is the study area for this research. Using a Domestic Consumption Monitor (DCM) from Yorkshire Water, which contains the water consumption of a sample of households, water use is matched to the synthetic population to produce baseline demand for the city, which equates to 106 million litres per day or 148 litres per person per day, which approximates the UK average. The research then involved designing and administering a behavioural survey, which was informed by a review of other surveys that have been undertaken in the UK. The survey of more than 1,000 individuals was found to contain a representative sample by housing type and metered versus non-metered houses when compared to Leeds and England. Moreover, the number of water ecologists and water utilitarians was also evenly distributed. The results of the survey showed a number of findings regarding water conservation behaviour and measures that might encourage conservation. For example, water efficient showerheads, water displacement devices in a cistern, installing a water butt, installing a dual/low flush toilet and not washing food and vegetables under a tap are all behaviours that people would adopt in the future. The survey was then used to calculate the likelihood or probability that households would adopt a particular water conservation behaviour, disaggregated by different demographic variables such as housing type, tenure, age and social economic group. These probabilities formed the basis of scenarios in which the water savings from a particular behaviour were applied to the synthetic household population to determine overall water savings by ward and for the city of Leeds as a whole. Scenarios involving a single behavioural change and multiple behaviours together were investigated. A sensitivity analysis was applied to these results to consider over-estimation in both the probabilities of likely adoption of a particular behaviour in the future and the amount of water that would be saved by adopting the behaviour. The results showed that the maximum possible savings under the most optimistic multi-behavioural scenario is 30%. Given a more realistic scenario of adoption of the three most likely behaviours from the survey, the maximum potential savings are on the order of 7%.

Rapid population growth over the 20th century and changing climate has put many urban water supply systems under pressure around the world. Such pressure also exerted on most of the Australian water supply systems, which has led to the introduction of water use restrictions to ensure environmentally sustainable water supply. To operate cost effective and reliable urban water supply systems, analysing urban water use and forecasting future water demand is an essential task. Generally, the urban water use classified as residential and non-residential water use based on different activities. In Melbourne (Australia), water authorities have used end-use models to forecast water demand, in which the residential component is extensively modelled. In these end-use models, the total household water use is broken down to the end-use level (e.g. toilets, showers, washing machines, etc.) for forecasting water demand in the residential sector. However, a simple historical trend-based annual water demand is considered for the non-residential sector, as a whole. No temporal (i.e. quarterly or monthly) and spatial disaggregation were considered in the non-residential water demand forecasts in these end-use models. It was also found that the existing work around the world on water demand modelling mainly focused on residential water use modelling. However, a significant portion of urban water usage is nonresidential. For example, around 25% of the total water use in Melbourne is used by the non-residential sector. Therefore, the modelling of non-residential urban water use has significant importance for effective water supply system in any urban area. Considering this knowledge gap for effective urban water supply, this project aims to forecast short term (i.e. month to year) non-residential water demand which is useful for system operation as well as budgeting and financial management. To achieve this aim, the water use billing data for each non-residential customer located in the Yarra Valley Water service area (in Melbourne, Australia) were used for developing non-residential water demand models in this research. All customers were disaggregated into several groups based on the homogenous water activity such as Schools, Sports Grounds, Councils, Restaurants, Hospitals, Hotels, and Laundries. The high water users (>50 ML/year) were also considered as a separate group in this study named as High Water Users. All customers in the homogenous groups were further divided into smaller groups based on the annual water use (>20 ML, >15-20 ML, >10-15 ML, 5-10 ML, and <5 ML). Data analysis was then carried out for each of these user groups to identify the water use pattern. Data analysis showed that there were some seasonal effects on Schools, Sports Grounds and Councils. Therefore, water use among these groups was modelled using the Multiple Linear Regression (MLR) technique with the available climatic variable and water restrictions data. In the remaining groups no seasonal variations were identified during data analysis. Moreover, most of their water uses are for indoor purposes and therefore, water use modelling was carried out for these remaining groups with the past water use data only due to unavailability of data for other influential factors. All forecasting models developed in this research were validated with the observed data and the model performance was measured with the Nash-Sutcliffe efficiency criteria. Results showed that most of these developed models performed well except for few cases. Some issues and challenges were also identified during models development among the homogenous groups in non-residential sectors. All these issues and challenges are listed in this thesis for future research. The major innovation of this study was the development of the disaggregation approach for sector based non-residential water demand modelling. This approach is successfully demonstrated in this research by disaggregating customers based on their activity and their annual water use. The development of non-residential water demand models at individual customer level is also the knowledge advancement, as limited work was found in this area.

Existing travel patterns in Windhoek are influenced by the past land use policy of restricted urban development in the former townships ofKatutura and Khomasdal. Thus residents in these townships are faced with longer distances to reach quality urban servtces. This thesis examined the impact on travel demand of changes in land use policies aimed at stimulating growth in the two areas using the HLFM computer model. The results showed that, policy induced land use changes may stimulate population and employment growth in the neglected areas, but with no effects on the present travel patterns. The model needs to be refined to fit the peculiarity of the study area. Nevertheless, information provided in the thesis could be used in assessing areas where development trends will go if any ofthe land use policy tested in the study is adopted.
Geography
M.A. (Geography)

In recent times, urban water utilities have shown increasing preference for increasing block tariff (IBT) for pricing domestic water. Although appealing at the outset, IBT’s can fall short of achieving its goals such as adequate cost recovery and effective cross-subsidization when water utilities arbitrarily design the IBT structure without any rational basis. To solve this problem, this study presents a simulation method to design an IBT for Bangalore, taking into consideration the nature of distribution of monthly consumption of domestic households and the revenue requirements of the water utility. The simulation method aims to find the best set of IBT parameters that simultaneously satisfies goals such as cost recovery, affordability, cross-subsidization and most importantly, fairness of marginal prices in the higher blocks of IBT. The study presents the design of a 3 block IBT for pricing domestic water in Bangalore and finds that an increase of the marginal price in the primary IBT block increases the likelihood of achieving the IBT goals simultaneously, when compared to marginal prices in the higher blocks. At the same time, the block sizes have a negative significant influence on achieving the IBT goals simultaneously. The relative influence of the 3 block IBT parameters on achieving the IBT goals simultaneously is assessed using logistic regression and the IBT parameter solution set is represented using a decision tree. In addition, the study analyses the IBT block switching characteristics of domestic households in Bangalore, during the time periods (2006-2009 & 2016-2018), using a steady state model and also reports the price elasticity of IBT. In contrast to previous studies that have assumed the positive association between household income levels and household consumption, a pre-requisite condition for effective implementation of an IBT, this study clearly demonstrates the positive influence of the household income on the monthly average household consumption by developing water demand models at aggregate municipal ward level and household level. First, the aggregate water demand model is developed for 198 municipal wards of Bangalore, using average monthly household water consumption as dependent variable and a set of demographic variables namely population density, household density etc. and infrastructural covariates such as average built-up area, property density, road density, per capita park area etc. Results suggest that average built up area and per capita park area have a significant positive influence on the average monthly household consumption. Owing to the availability of few extra covariates in the 39 municipal wards of South Bangalore, the study also develops an aggregate water demand model for the 39 municipal wards in South Bangalore. Analysis reveals that average household water consumption is higher in municipal wards, where the fraction of houses with more than 3 rooms is higher. Second, the study develops a household water demand model for Bangalore city, using survey data from 300 households in different regions of Bangalore. Analysis reveals that the household domestic consumption increases with respect to demographic variables such as household income, family size, educational level and infrastructural variables such as number of bedroom, bathrooms and presence of washing machine. The study also finds that the practice of sharing of water meters by multiple households increases the average household water consumption.