Modelling of individual domestic occupancy and energy demand behaviours using existing datasets and probabilistic modelling methods

Flett, Graeme; Kelly, Nick

doi:10.1016/j.enbuild.2021.111373

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…For urbanized, grid-connected settings, there exist load profile models that focus on both electricity and heat [10], as well as some that focus specifically on heat [11]. More recently, efforts have focused on developing advanced methods for stochastic generation of load profiles for electricity [12] and heat [13,14] in order to better consider the uncertainty associated with occupant behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating thermal energy loads in remote and northern communities to facilitate a net-zero transition

Maynard

Abdulla

2023

Environ. Res.: Infrastruct. Sustain.

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Canada has more than 350 remote and northern communities, most of which rely on diesel for their electric and thermal needs. This reliance is deleterious to climate, health, albedo, and energy security—all diesel must be imported. The government is working to transition these communities to climate-friendly and sustainable alternatives, but assessments of this transition are hampered by limited data availability, especially the absence of hourly thermal load profiles. Here, we develop a method for estimating the thermal load profiles of these communities; apply it to 40 communities that vary across characteristics like population, location, accessibility, and Indigenous identity; and seek to validate these profiles with the few empirical data that exist. We also develop a model to predict the thermal load of a remote and northern community using limited, available information like population and location. This paper represents the first attempt to simulate hourly thermal load profiles for these communities. We find that thermal loads are large—the hourly thermal load can be up to 23 times the hourly electrical load in winter, which has implications for investment planning. Our research helps communities, investors, and analysts develop robust transition plans as they seek to decarbonize northern communities’ energy systems.

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Section: Literature Reviewmentioning

confidence: 99%

Estimating thermal energy loads in remote and northern communities to facilitate a net-zero transition

Maynard

Abdulla

2023

Environ. Res.: Infrastruct. Sustain.

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“…In particular, Aerts et al (2014) conducted cluster analysis based on occupancy patterns and proposed a series of diverse occupancy models. In addition, Flett and Kelly (2021) used statistical methods to develop a model for simulating the diversity of user behaviors. The second stochastic model subtype comprises multi-factor estimation models used for predicting the mean occupancy based on multiple factors, such as time, environmental parameters, and user behavior.…”

Section: ) Stochastic Modelmentioning

confidence: 99%

Occupancy of rooms in urban residential buildings by users in cold areas of China

Dong

Sun

2022

Build. Simul.

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Occupancy is used to represent the movements and locations of users among various zones of buildings, and it is the basis of all other daily energy consumption behaviors. This study investigated eight families in cold areas of China based on occupancy measurements obtained in four main rooms, i.e., living room, bedroom, kitchen, and bathroom. In particular, we analyzed the duration of user occupancy and hourly mean occupancy, and characterized their regular and random features. According to the results, we developed an event-based occupancy model using an inhomogeneous Markov chain, where the rooms were modeled and daily events were divided into three categories according to their randomness. We established a new method for conversion between event characteristic parameters and a transition probability matrix, as well as an overlap avoidance method for active events. The model was then validated using real data. The results showed that the model performed well in terms of two evaluation criteria. The model should improve the accuracy of simulations of occupancy.

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“…In fact, factors influencing the energy use of a building are multiple and are clearly outlined in the literature: occupancy profiles [2][3][4][5][6], appliance schedules [7,8], and human behaviour [9,10] are involved in the final energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…The recent work by Flett et al [3] highlights the importance of realistic occupancy models and the consequent electrical demand, by using and adapting existing datasets and probabilistic modelling methods, whilst in [4], the occupancy profiles of households allowed identifying peak load and, therefore, energy-saving margins and opportunities within the peak load shift.…”

Section: Introductionmentioning

confidence: 99%

Energy and Environmental Effects of Human Habits in Residential Buildings Due to COVID-19 Outbreak Scenarios in a Dwelling near Rome

2021

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The COVID-19 pandemic and resulting containment measures have shown that energy consumption in buildings is linked to several factors, such as living habits, occupancy profiles, and heating ventilation and air conditioning (HVAC) systems. This paper addresses the influences of such factors on energy consumption in a residential building, analysing different scenarios (pre-COVID-19, lockdown, post-COVID-19), in terms of discomfort and energy needs, through the new hourly calculation method (UNI EN ISO 52016). Energy and environmental effects were studied in a real case study near Rome by varying occupancy profiles, lighting and appliance schedules, and HVAC systems. Results show that, during the heating period, the lockdown scenario led to the lowest hours of discomfort (−29% on average), but the highest in the cooling period (up to +154%, +28% on average). The same scenario led to reasonable reduction of energy needs for heating (−14%), but also highlighted a significant increase (+60%) for the cooling period. This study underlines how the pandemic has influenced the energy and environmental behaviours in buildings. Moreover, the new hourly calculation method points out the importance of analysing HVAC systems, in terms of hours of discomfort, which could provide results that are more reliable.

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Modelling of individual domestic occupancy and energy demand behaviours using existing datasets and probabilistic modelling methods

Cited by 8 publications

References 23 publications

Estimating thermal energy loads in remote and northern communities to facilitate a net-zero transition

Estimating thermal energy loads in remote and northern communities to facilitate a net-zero transition

Occupancy of rooms in urban residential buildings by users in cold areas of China

Energy and Environmental Effects of Human Habits in Residential Buildings Due to COVID-19 Outbreak Scenarios in a Dwelling near Rome

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