Save Money or Feel Cozy?: A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences

We present the design of a fully autonomous smart thermostat that supports end-users in managing their heating preferences in a realtime pricing regime. The thermostat uses a machine learning algorithm to learn how a user wants to trade off comfort versus cost. We evaluate the thermostat in a field experiment in the UK involving 30 users over a period of 30 days. We make two main contributions. First, we study whether our smart thermostat enables end-users to handle real-time prices, and in particular, whether machine learning can help them. We find that the users trust the system and that they can successfully express their preferences; overall, the smart thermostat enables the users to manage their heating given real-time prices. Moreover, our machine learning-based thermostats outperform a baseline without machine learning in terms of usability. Second, we present a quantitative analysis of the users’ economic behavior, including their reaction to price changes, their price sensitivity, and their comfort-cost trade-offs. We find a wide variety regarding the users’ willingness to make trade-offs. But in aggregate, the users’ settings enabled a large amount of demand response, reducing the average energy consumption during peak hours by 38%

Incentives and Two-Sided Matching - Engineering Coordination Mechanisms for Social Clouds

The Social Cloud framework leverages existing relationships between members of a social network for the exchange of resources. This thesis focuses on the design of coordination mechanisms to address two challenges in this scenario. In the first part, user participation incentives are studied. In the second part, heuristics for two-sided matching-based resource allocation are designed and evaluated