Scalable and Personalized Energy Efficiency Services with Smart Meter Data

Information and communication technology plays an important role in addressing the world’s energy problem. Networked digital electricity meters (so-called smart meters), for instance, can provide households with real-time information on their electricity consumption and thus help them to conserve energy. Initial expectations on the saving potential of this technology were too optimistic, however. In fact, recent pilot studies conducted under realistic assumptions have shown that savings induced by plain electricity consumption feedback are often significantly lower than many have originally expected. In this dissertation, we take smart metering to a new level as we explore a data analysisdriven approach to personalize energy efficiency services that may be offered at large scale. An example for such a service is automated energy consulting, which consists in automatically providing energy saving recommendations to households by taking into account their appliance stock and usage profiles. In addition, we provide the foundation for an electricity bill that is tailored to the household as it shows the contribution of individual appliances to the overall bill or compares a household’s consumption with other households that have similar characteristics. Behavioral trials indicate that such consumption feedback is potentially more successful in motivating households to reduce their electricity consumption than plain consumption feedback or generic energy saving recommendations. One contribution of this thesis is the design, development, and evaluation of a system that automatically estimates characteristics of a household (like its socio-economic status, dwelling properties, and appliance stock) from the household’s electricity consumption data. We evaluate our approach on real world smart meter data collected from more than 4000 households over a period of 1.5 years. Our analysis shows that inferring household characteristics is feasible, as our method achieves an accuracy of more than 70% over all households for many of the characteristics and even exceeds 80% for some of the characteristics. For utilities, the system creates valuable customer insights