Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach

Bustos-Turu, Gonzalo; Dam, Koen H. van; Acha, Salvador; Markides, Christos N.; Shah, Nilay

doi:10.1109/energycon.2016.7514077

Cited by 17 publications

(12 citation statements)

References 15 publications

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“…Most respondents in the follow-up interviews could be classified as "continuous adapters", based on their self-reported interaction frequency with the different interfaces. While the sample of respondents in this study is too small to draw any conclusions about the different user profiles, this characterisation of user typologies may be a useful indicator of the flexibility which DHN operators must build into their efforts to forecast heating demand [20] and estimate their building's energy performance [69]. For example, the high prevalence of users fitting the "continuous adapter" profile indicates that most respondents may tweak their schedules and set-points frequently.…”

Section: Use Of Heating Schedulesmentioning

confidence: 99%

“…This action not only improves heating behaviour efficiency by reducing unnecessary consumption, but also increases the likelihood that heating demand patterns will match occupancy patterns. Residential occupancy patterns are often used to model domestic energy and heat demand; therefore, the closer they are to actual heating demand patterns, the higher the accuracy of demand models [20]- [22], which is central to DHNs' operational efficiency and associated cost savings and environmental benefits [23]- [25]. In addition to the existence or absence of heating schedules, the study looked at other potential indicators of heating behaviour efficiency.…”

Section: Figure 2 Smart Heating Controls: Thermostat With Control DImentioning

confidence: 99%

“…On the other hand, the use of these programming features to create heating schedules is a specific type of behaviour with important ramifications for heating demand forecasting and management; in particular for matching demand to supply, a high-potential efficiency improvement for the British residential sector [49]. Specifically, [20] pinpoint that accurate heating demand forecasting and management is reliant on the reflection of actual occupancy patterns in heating demand: in building energy demand models, occupancy patterns are frequently assumed to be a good predictor of actual heating demand. However, this does not take into account the fact that residents may actually have their heating on or off constantly, to avoid the complicated task of setting up a heating schedule [36].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Going smart, staying confused: Perceptions and use of smart thermostats in British homes

Miu

Mazur

Dam

et al. 2019

Energy Research & Social Science

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Section: Use Of Heating Schedulesmentioning

confidence: 99%

Section: Figure 2 Smart Heating Controls: Thermostat With Control DImentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Going smart, staying confused: Perceptions and use of smart thermostats in British homes

Miu

Mazur

Dam

et al. 2019

Energy Research & Social Science

Self Cite

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“…The resulting model formulations are suitable for advanced control, while accuracy over the relevant time horizons can be achieved, but only with sufficiently excited training data [74]. To capture the behaviour and interactions of users with the energy systems and thus better predict energy demands, agent-based approaches can be implemented, such as in [75]. Time-series approaches for forecasting building energy consumption are reviewed in [76], while electrical load forecasting techniques reviewed in [77].…”

Section: Modelling the Thermal Behaviour Of Buildingsmentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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“…This trend emphasises the importance of quantifying the influence of occupants on energy consumption. On the city scale, former work includes high resolution energy models without explicit influence of occupancy [5], [15], [16], and models including people behaviour but only in aggregated form on lower spatial resolution [17].…”

Section: Introductionmentioning

confidence: 99%

Occupancy Based Thermal Energy Modelling in the Urban Residential Sector

Tröndle¹,

Choudhary²

2017

WIT Transactions on Ecology and the Environment

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Energy use in the urban residential sector corresponds directly to operation of energy consuming devices. For instance, space-heating is a substantial element of residential energy consumption in the UK, but it is as much tied to occupant preferences and timing of their presence in a home, as it is to the physical characteristics of the dwelling. Timing, duration, and in-use efficiency of residential energy consumption are essential for increasing the utilization of district energy systems and demand-side management. The ongoing IEA-EBC Annex 66 and our own recent work attests that there is a need to develop new methods to represent occupant presence and energy consuming activities in energy demand modelling. This is as much a multi-scale simulation issue, as it is a computational challenge. This paper presents a proof-of-concept methodology of estimating thermal energy demand on the urban scale by introducing occupancy models to high resolution bottom-up energy models. A synthetic population is created from census data and the occupancy of every citizen is modelled using a time heterogeneous Markov chain which is calibrated using time use survey data. The methodology is applied to a case study where the thermal energy demand is found to be varying up to 50% in different locations at certain times of the week. Regions with less diverse energy demand and thermal power patterns can be identified and discriminated against those with more diversity in the demand. Keywords: residential building occupancy, building energy model, agent-based model, population synthesis, micro-simulation. INTRODUCTIONAccounting for 34% of global energy end-use, the building sector is the largest energy sink and a major contributor to global CO 2 emissions [1]. Three quarters of this amount are accountable for space heating and cooling purposes. When trying to reduce this energy impact, understanding the energy demand originating in the building sector and its drivers is crucial. With more than half of the global population living in cities and with on-going urbanisation [2], urban built environments are becoming more important in this regard. This is even more so true when looking at America or Europe where the urban population exceeds 70% of the total population already today [2]. Estimations of energy demand for space heating are valuable during the design phase of urban built environment and its energy supply infrastructure, but also for effective retrofitting actions and incremental performance evaluations. In particular, the task of designing efficient energy supply systems asks for a high temporal resolution of the estimation [3]. The built infrastructure and other factors impacting energy demand for space heating are diversely distributed in urban environments and hence analyses have recently been done on a high spatial resolution as well [4]-[6].While thermodynamic models of buildings are well understood and assumed to achieve good results, simulated energy demand for space heating in urban environments deviate largely f...

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Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach

Cited by 17 publications

References 15 publications

Going smart, staying confused: Perceptions and use of smart thermostats in British homes

Going smart, staying confused: Perceptions and use of smart thermostats in British homes

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Occupancy Based Thermal Energy Modelling in the Urban Residential Sector

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