The increase of energy efficiency and reduction of greenhouse gas emissions have become important targets of EU initiatives (European Commission 2014). Emissions from personal vehicles are a key cause of worldwide greenhouse gas emissions with 90% of personal transport emissions being caused by private vehicles (Barkenbus 2010). Energy efficient driving techniques, or eco-driving, can realize energy savings up to 20% (Stillwater et al. 2012). Combined with behavioral approaches, which are increasingly being used by governments (Cabinet Office 2012), these techniques represent a promising way to increase energy efficiency in the transport sector. Yet, practical solutions which are based on scientific findings are scarce. Since mobile apps are versatile in their functionality, characterized by a short time to market and low costs, they can represent a solution. The research question therefore is: How can a mobile application contribute to energy-efficient driving?
The water conservation campaigns in residential households are hindered by the poor understanding of residents of how much water they use. For the better designed interventions new tools are necessary to educate the consumers on the water usage of different consumption events. In this paper we use the fine grained (0.5 Hz) water consumption data that was collected non- intrusively in a household over the period of 21 days to develop such tools. We examine the collected data and disaggregate the consumption events into three different categories: short events (e.g., toilet flush), long regular events (e.g., washing machine) and long irregular events (e.g., showers). To achieve this, we use clustering methods, based on level set trees, to identify groups of events that are similar to each other.