# Interactive and Dynamic Simulation of Energy and People Activity in Urban Built Environments

## Introduction and Related Works

## Methodology

### Energy Simulation

### People Activity

### Interactive Features

## Results

Comparing results with and without people activity against measured data.

## Conclusion

# Energy Impact of Occupant Activity in Urban Built Environments

## Abstract

Occupants are driving the energy demand in buildings, either directly for example through the use of appliances, or more indirectly through comfort preferences like heating set points and the energy demand of auxiliary functions like fridges. The significance of occupants and their activities and behaviours for energy demand estimations has been identified in former studies and a lot of work is undertaken in quantifying this relationship and explicitly modelling occupants to understand and predict choices of activities and behaviours and their impact on energy demands in single households, public buildings and offices, and settlements of detached buildings.

On the district and urban scale occupants are still typically represented by implicit and general load profiles ignoring occupant activities as the main driver for energy demand, and ignoring spatial and temporal dependencies. This work aims at identifying the impact of occupant activity on the heating and cooling energy demand in buildings on an urban scale by representing occupants as agents in a city-wide model. 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

## Methodology

### Energy Simulation

See description of [conceptual model](https://github.com/timtroendle/spatial-cimo/blob/develop/doc/conceptual-model.md).

Mainly focusing on domestic buildings.

### Occupant Activity

See description of [conceptual activity model](https://github.com/timtroendle/spatial-cimo/blob/develop/doc/conceptual-activity-model.md).

### (Optional) Identification of control strategies

Based on measured data. Identify which control strategy is likely to be used by which fraction of households in spatial area.

## Results

Validate simulation results. Goal: stay in certain *plausible* ranges.

Compare heating/cooling energy needed for different HVAC control strategies.

Optional: Discuss likelihood of certain HVAC control strategies for certain spatial areas. For example the fraction of an LSOA following a certain control strategy.

## Conclusion

Add first abstract draft and updated structure
Add first abstract draft and updated structure
