QUARREE100

Introduction

The Q-Scope framework was used in the QUARREE100 project. Before diving into the detailed programming of the framework, you should understand how we used it in the “pilot project” QUARREE100. This documentation will frequently refer to the setup we implemented for the project. QUARREE100 is a research project investigating the possibilities of renewable heating of an existing neighborhood. The Q-Scope was used to further investigate citizens’ acceptance of the progress by providing a platform to interact with simulation results regarding energy prices and CO2-emissions.

QUARREE100 user interface

After startup, you see an aerial photograph of the area of importance for the QUARREE100 project, projected onto the table. All existing buildings are displayed, some marked red. These colored buildings are the ones you can interact with using some of the tokens explained below. On the right side, there is an area for global settings, that is to be used only by the “game master” - a person that guides the whole group of users through the workshop. The area contains some positions to switch through the game modes and a range of fields for some gameplay scenarios.

For the general user interaction, all users get two tokens with which they can select buildings from the map. By placing one of their tokens onto one of the red houses, the users will be able to see the buildings’ address and energy saving on the infoscreen.

The aim of the QUARREE100 game is to reduce the emissions and lower the energy costs. In order to achieve this, users can choose from (combinations of) options concerning the heating of their house. There are three options to choose from:

1. energy-saving behavior

2. conducting the renovation of the house

3. connecting the house to the QUARREE100-heat-grid

In order to choose from one of these, users have to put one of their tokens into one of the fields above any of the sliders. Each of the fields represents one of the beforementioned actions. The slider will be activated so that one can define the magnitude of their action. Changes will be applied to all houses you have selected simultaneously.

After all users are done with the adjustments, the game master can start the simulation by putting any non-white token onto the global simulation position. The Agent-Based model is thus started and calculates the data for the adjusted buildings.

Now you also see predictions of CO2-emissions and energy costs, pre-calculated by the agent-based model. The data is plotted on a timeline up until the year 2045.

The game master can now discuss the calculated data with the users in two different visualization modes. Furthermore, they can pre-define the number of buildings connected to the heat grid to influence the calculation of neighborhood emissions and energy costs.

For the discussion, it was necessary to both focus on individual households’ energy costs and emissions, as well as being able to compare the data of various households between each other.