<commit_before># Agent-Based Modelling of Occupant Presence in Residential Built Environments

// TODO update title

## Abstract

// TODO update abstract

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 characterstics 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 of (eg. through time-of-use tariffs). The study of occupant driven activities is inherently stochastic and multi-disciplinary. 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. Indeed, few studies investigate micro-use patterns of energy use in detail at the urban-scale. This is as much a multi-scale simulation issue, as it is a computational challenge.

Agent-Based Models (ABMs) are able to simulate the distribution of people across the city at high spatial and temporal resolutions. This paper presents a proof-of-concept ABM model in....

Two step approach: (a) occupant presence or absence (b) occupant behaviour when present (setpoint) or absent (heating on timer)

In the near future, we envision that these models will also become crucial tools in optimizing the use of future digital innovations, e.g. smart metering and Internet of Things.

This work aims at identifying the impact of occupant 'presence' on the heating and cooling energy demand in buildings on an urban scale by representing occupants as agents in a city-wide model with high spatial and temporal resolution. The agent-based approach of the model allows for representing spatial variability and analysing space-time relationships by linking energy usage in space and time through occupants, their locations and activities. The model is applied in a case study of London.

## Introduction and Related Works

* heating system set points have high impact on building energy usage, as shown in former research

* if, contrary to normative building energy assessment, one is interested in _actual_ energy usage and not normative energy usage, exact heating set points are hence of high importance

* generally, the heating set point for a heating Zone z can be described by &theta;<sub>set, z</sub> = &theta;<sub>set, z</sub>(L<sub>P<sub>z</sub></sub>, A<sub>P<sub>z</sub></sub>, B<sub>P<sub>z</sub></sub>), where:

    * P<sub>z</sub>: set of people inside the heating zone or related to it
    * L<sub>P<sub>z</sub></sub> location of People P<sub>z</sub>
    * A<sub>P<sub>z</sub></sub> activity of People P<sub>z</sub>
    * B<sub>P<sub>z</sub></sub> heating behaviour (comfort zone, awareness, financial situation, usage pattern) of People P<sub>z</sub>

* for all (comfort zone, location, activity, heating behaviour), the social context is important, which brings spatial dimension into play // TODO rational for the need of spatial dimension still weak

## Methodology

### Conceptual Model

#### Set Point Model

Here the following simplifications are made compared to the general model above:<commit_msg>Add motivation detail<commit_after># Agent-Based Modelling of Occupant Presence in Residential Built Environments

// TODO update title

## Abstract

// TODO update abstract

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 characterstics 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 of (eg. through time-of-use tariffs). The study of occupant driven activities is inherently stochastic and multi-disciplinary. 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. Indeed, few studies investigate micro-use patterns of energy use in detail at the urban-scale. This is as much a multi-scale simulation issue, as it is a computational challenge.

Agent-Based Models (ABMs) are able to simulate the distribution of people across the city at high spatial and temporal resolutions. This paper presents a proof-of-concept ABM model in....

Two step approach: (a) occupant presence or absence (b) occupant behaviour when present (setpoint) or absent (heating on timer)

In the near future, we envision that these models will also become crucial tools in optimizing the use of future digital innovations, e.g. smart metering and Internet of Things.

This work aims at identifying the impact of occupant 'presence' on the heating and cooling energy demand in buildings on an urban scale by representing occupants as agents in a city-wide model with high spatial and temporal resolution. The agent-based approach of the model allows for representing spatial variability and analysing space-time relationships by linking energy usage in space and time through occupants, their locations and activities. The model is applied in a case study of London.

## Introduction and Related Works

* heating system set points have high impact on building energy usage, as shown in former research

* if, contrary to normative building energy assessment, one is interested in _actual_ energy usage and not normative energy usage, exact heating set points are hence of high importance

* predictions of status quo could be made with data analysis, but predictions involving system changes, simulation is necessary

* generally, the heating set point for a heating Zone z can be described by &theta;<sub>set, z</sub> = &theta;<sub>set, z</sub>(L<sub>P<sub>z</sub></sub>, A<sub>P<sub>z</sub></sub>, B<sub>P<sub>z</sub></sub>), where:

    * P<sub>z</sub>: set of people inside the heating zone or related to it
    * L<sub>P<sub>z</sub></sub> location of People P<sub>z</sub>
    * A<sub>P<sub>z</sub></sub> activity of People P<sub>z</sub>
    * B<sub>P<sub>z</sub></sub> heating behaviour (comfort zone, awareness, financial situation, usage pattern) of People P<sub>z</sub>

* for all (comfort zone, location, activity, heating behaviour), the social context is important, which brings spatial dimension into play // TODO rational for the need of spatial dimension still weak

## Methodology

### Conceptual Model

#### Set Point Model

Here the following simplifications are made compared to the general model above:
{'fraction_non_alphanumeric': 0.06191639132308576, 'fraction_numerical': 0.0005746300818847866, 'mean_word_length': 4.825941422594142, 'pattern_counts': {'":': 0, '<': 75, '<?xml version=': 0, '>': 75, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '444399', 'n_tokens_mistral': 1681, 'n_tokens_neox': 1593, 'n_words': 969}