Zukünftige Energie- und Industriesysteme
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A key factor to energy-efficiency of heating in buildings is the behavior of households, in particular how they ventilate rooms. Energy demand can be reduced by behavioral change; devices can support this by giving feedback to consumers on their behavior. One such feedback device, called the "CO2 meter", shows indoor air-quality in the colors of a traffic light to motivate so called "shock ventilation", which is energy-efficient ventilation behavior. The following effects of the "CO2 meter" are analyzed: (1) the effect of the device on ventilation behavior within households, (2) the diffusion of "CO2 meter" to other households, and (3) the diffusion of changed behavior to households that do not adopt a "CO2 meter". An agent-based model of these processes for the city of Bottrop (Germany) was developed using a variety of data sources. The model shows that the "CO2 meter" would increase adoption of energy-efficient ventilation by c. 12% and reduce heating demand by c. 1% within 15 years. Technology diffusion was found to explain at least c. 54% of the estimated energy savings; behavior diffusion explains up to 46%. These findings indicate that the "CO2 meter" is an interesting low-cost solution to increase the energy-efficiency in residential heating.
Heating behavior of households is key for reducing domestic energy demand and mitigating climate change. Recently, various technical devices have been developed, providing households with feedback on their heating behavior and supporting energy conservation behavior.
The impact of such devices on overall energy consumption depends on (1) the impact of a device within a household, (2) the diffusion of devices to other households and the number of adopters, and (3) the diffusion of the induced behavioral change beyond these households. While the first two processes are currently established in assessments of sustainable household devices, we suggest that adding behavior diffusion is essential when assessing devices that explicitly target behavioral change. We therefore propose an assessment framework that includes all three processes. We implement this framework in an agent-based model by combining two existing simulation models to explore the effect of adding behavior diffusion. In three simulation experiments, we identify two mechanisms by which behavior diffusion (1) spreads the effect of such devices from adopters to non-adopters and (2) increases the average speed of behavioral change of households. From these results we conclude that behavior diffusion should be included in assessments of behavior-changing feedback devices.