Relevant bibliographies by topics / Residential demand modelling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Residential demand modelling'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Residential demand modelling"

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Dilaver, Zafer, and Lester C. Hunt. "Modelling and forecasting Turkish residential electricity demand." Energy Policy 39, no. 6 (June 2011): 3117–27. http://dx.doi.org/10.1016/j.enpol.2011.02.059.

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Assimakopoulos, V. "Residential energy demand modelling in developing regions." Energy Economics 14, no. 1 (January 1992): 57–63. http://dx.doi.org/10.1016/0140-9883(92)90025-9.

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Worthington, Andrew C., and Mark Hoffman. "AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING." Journal of Economic Surveys 22, no. 5 (July 24, 2008): 842–71. http://dx.doi.org/10.1111/j.1467-6419.2008.00551.x.

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Atalla, Tarek N., and Lester C. Hunt. "Modelling residential electricity demand in the GCC countries." Energy Economics 59 (September 2016): 149–58. http://dx.doi.org/10.1016/j.eneco.2016.07.027.

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Alcocer Yamanaka, Víctor Hugo, and Velitchko G. Tzatchkov. "Neyman-Scott-based water distribution network modelling." Ingeniería e Investigación 32, no. 3 (September 1, 2012): 32–36. http://dx.doi.org/10.15446/ing.investig.v32n3.35937.

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Residential water demand is one of the most difficult parameters to determine when modelling drinking water distribution networks. It has been proven to be a stochastic process which can be characterised as a series of rectangular pulses having set intensity, duration and frequency. Such parameters can be determined using stochastic models such as the Neyman-Scott rectangular pulse model (NSRPM). NSRPM is based on resolving a non-linear optimisation problem involving theoretical moments of the synthetic demand series (equiprobable) and of the observed moments (field measurements) statistically establishing the measured demand series. NSRPM has been applied to generating local residential demand. However, this model has not been validated for a real distribution network with residential demand aggregation, or compared to traditional methods (which is dealt with here). This paper compares the results of synthetic stochastic demand series (calculated using NSRPM applied to determining pressure and flow rate) to results obtained using traditional simulation methods using the curve of hourly variation in demand and to actual pressure and flow rate measurements. The Humaya sector of Culiacan, Sinaloa, Mexico, was used as study area.
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Megri, Ahmed Cherif, and Yao Yu. "Study of residential underfloor air distribution (UFAD) systems using a new modelling approach." Indoor and Built Environment 26, no. 1 (July 28, 2016): 5–20. http://dx.doi.org/10.1177/1420326x15597544.

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Although single/multi-zone thermal models have their own advantages, like simple and fast computations of building energy demand, the accuracy of these models is problematic. The assumption of a uniform room temperature reduces the accuracy of the final energy demand results. In fact, the single/multi-zone thermal models are not able to predict indoor thermal behaviours or building energy demands accurately, if a non-uniform environment in a room or building is created by a single or multiple heating, ventilation, and air conditioning (HVAC) systems, i.e. an underfloor air distribution (UFAD) system. The research described in this article investigated the use of a new approach to improve the computational quality and accuracy of the heating energy demands of UFAD systems using an integrated zonal/multi-zone model. Several case studies were carried out, and the results demonstrate not only the advantages of UFAD systems used in a residential house in terms of energy saving, but also the importance of thermostat location in the prediction of building energy consumption. Additionally, the results indicate that the conventional single/multi-zone models are not appropriate to use for UFAD systems in the building energy demand predictions.
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Starr, Claudia, Thomas G. Cowing, and David L. McFadden. "Microeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of the Operational Research Society 37, no. 8 (August 1986): 823. http://dx.doi.org/10.2307/2581969.

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Chatterjee, Samprit, Thomas G. Cowing, Daniel L. McFadden, and Paul C. Stern. "Macroeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of Business & Economic Statistics 3, no. 4 (October 1985): 413. http://dx.doi.org/10.2307/1391737.

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Starr, Claudia. "Microeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of the Operational Research Society 37, no. 8 (August 1986): 823–24. http://dx.doi.org/10.1057/jors.1986.145.

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Ben Zaied, Younes, and Marie Estelle Binet. "Modelling seasonality in residential water demand: the case of Tunisia." Applied Economics 47, no. 19 (January 21, 2015): 1983–96. http://dx.doi.org/10.1080/00036846.2014.1002896.

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Dissertations / Theses on the topic "Residential demand modelling"

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Gardner, Kerry. "Residential water demand modelling and behavioural economics." Thesis, University of East Anglia, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539372.

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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.
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Urban, Graeme John. "Probabalistic load modelling of electrical demand of residential water heating." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20071.

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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.
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Rahman, Md Moktadir. "Modelling and analysis of demand response implementation in the residential sector." Thesis, Rahman, Md Moktadir (2018) Modelling and analysis of demand response implementation in the residential sector. PhD thesis, Murdoch University, 2018. https://researchrepository.murdoch.edu.au/id/eprint/40779/.

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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.
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Boyce, Daniel J. B. "Micro-component water demand scenario modelling for catchment scale residential water use." Thesis, Cranfield University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443748.

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Gyamfi, Samuel. "Demand Response Assessment and Modelling of Peak Electricity Demand in the Residential Sector: Information and Communcation Requirements." Thesis, University of Canterbury. Mechanical Engineering Department, 2010. http://hdl.handle.net/10092/5063.

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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.
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Rahman, Mohammad Lutfur. "Modelling flyover induced travel demand in Dhaka, Bangladesh." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/113830/1/Mohammad%20Lutfur_Rahman_Thesis.pdf.

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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.
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Sancho, Tomás Ana. "Integrated modelling of electrical energy systems for the study of residential demand response strategies." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/46872/.

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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.
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Maurer, Nathalie. "Modelling urban development trends and outdoor residential water demand in the Okanagan Basin, British Columbia." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/17533.

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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.
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Tsagkarakis, George. "Domestic demand and network management in a user-inclusive electrical load modelling framework." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16207.

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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.
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Kelly, Scott. "Decarbonising the English residential sector : modelling policies, technologies and behaviour within a heterogeneous building stock." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244708.

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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.
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Books on the topic "Residential demand modelling"

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Elsland, Rainer. Long-Term Energy Demand in the German Residential Sector: Development of an Integrated Modelling Concept to Capture Technological Myopia. Nomos Verlagsgesellschaft, 2017.

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Book chapters on the topic "Residential demand modelling"

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Garbacz, Christopher. "Residential Electricity Demand Modelling with Secret Data." In Regulating Utilities in an Era of Deregulation, 137–54. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1007/978-1-349-08714-3_9.

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Bartlett, Sarita, Steinar Strøm, and Øystein Olsen. "Residential energy demand — the evolution and future potential of natural gas in Western Europe." In Recent Modelling Approaches in Applied Energy Economics, 29–47. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-011-3088-2_2.

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Maroufmashat, Azadeh, Q. Kong, Ali Elkamel, and Michael Fowler. "Modelling the Impact of Uncontrolled Electric Vehicles Charging Demand on the Optimal Operation of Residential Energy Hubs." In Electric Vehicles in Energy Systems, 289–312. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34448-1_12.

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Elsland, Rainer. "2. Relevant developments in the residential sector and existing modelling approaches." In Long-term Energy Demand in the German Residential Sector, 43–68. Nomos Verlagsgesellschaft mbH & Co. KG, 2016. http://dx.doi.org/10.5771/9783845267487-42.

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Elsland, Rainer. "3. Development of an integrated modelling concept for long-term energy demand analysis." In Long-term Energy Demand in the German Residential Sector, 69–78. Nomos Verlagsgesellschaft mbH & Co. KG, 2016. http://dx.doi.org/10.5771/9783845267487-68.

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Conference papers on the topic "Residential demand modelling"

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"Forecast of residential demand drivers for Greater Darwin." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.beatty.

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Pieterse-Quirijns, E. J., E. J. M. Blokker, J. H. G. Vreeburg, and E. v.d. Blom. "Modelling Characteristic Values for Non-Residential Water Demand." In 12th Annual Conference on Water Distribution Systems Analysis (WDSA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41203(425)111.

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Du, Baoxiang, Gregor Verbic, and John Fletcher. "Thermal modelling for demand response of residential buildings." In 2017 Australasian Universities Power Engineering Conference (AUPEC). IEEE, 2017. http://dx.doi.org/10.1109/aupec.2017.8282379.

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Tiedemann, Kenneth H. "Modelling Residential and Commercial Demand for Electricity Using Autoregressive Distributed Lag Models." In Modelling, Identification and Control / 827: Computational Intelligence. Calgary,AB,Canada: ACTAPRESS, 2015. http://dx.doi.org/10.2316/p.2015.826-013.

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Simani, Kyppy N., Yuval O. Genga, and Yu-Chieh J. Yen. "Using LSTM To Perform Load Modelling For Residential Demand Side Management." In 2023 31st Southern African Universities Power Engineering Conference (SAUPEC). IEEE, 2023. http://dx.doi.org/10.1109/saupec57889.2023.10057875.

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Tiedemann, Kenneth H. "Time-Series Modeling of the Demand for Energy Efficient Residential Appliances." In Visualization, Imaging and Image Processing / 783: Modelling and Simulation / 784: Wireless Communications. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.783-006.

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Ding, Y., and J. Yang. "An event-based flexible load modelling method for optimising residential demand response." In 11th International Conference on Renewable Power Generation - Meeting net zero carbon (RPG 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.1678.

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Jambagi, Akhila, Michael Kramer, and Vicky Cheng. "Residential electricity demand modelling: Activity based modelling for a model with high time and spatial resolution." In 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2015. http://dx.doi.org/10.1109/irsec.2015.7455047.

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Broka, Zane, Jevgenijs Kozadajevs, Antans Sauhats, Donal P. Finn, and William J. N. Turner. "Modelling residential heat demand supplied by a local smart electric thermal storage system." In 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2016. http://dx.doi.org/10.1109/rtucon.2016.7763128.

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Tardioli, Giovanni, Ruth Kerrigan, Mike Oates, James O'Donnell, and Donal Finn. "A Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings." In 2017 Building Simulation Conference. IBPSA, 2017. http://dx.doi.org/10.26868/25222708.2017.464.

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