An open solution to provide personalized feedback for building energy management

作者：Monacchi Andrea^*; Versolatto Fabio; Herold Manuel; Egarter Dominik; Tonello Andrea M; Elmehreich Wilfried

来源：Journal of Ambient Intelligence and Smart Environments, 2017, 9(2): 147-162.

DOI：10.3233/AIS-170422

• Home energy management
• persuasive interface
• smart metering
• behavior modeling
• ambient intelligence

The integration of renewable energy sources increases the complexity in maintaining the power grid. In particular, the highly dynamic nature of generation and consumption demands for a better utilization of energy resources, which seen the cost of storage infrastructure, can only be achieved through demand-response. Accordingly, the availability of energy and potential overload situations can be reflected using a price signal. The effectiveness of this mechanism arises from the flexibility of device operation, which is nevertheless heavily reliant on the exchange of information between the grid and its consumers. In this paper, we investigate the capability of an interactive energy management system to timely inform users on energy usage, in order to promote an optimal use of local resources. In particular, we analyze data being collected in several households in Italy and Austria to gain insights into usage behavior and drive the design of more effective systems. The outcome is the formulation of energy efficiency policies for residential buildings, as well as the design of an energy management system, consisting of hardware measurement units and a management software. The Mjolnir framework, which we release for open use, provides a platform where various feedback concepts can be implemented and assessed. This includes widgets displaying disaggregated and aggregated consumption information, as well as daily production and tailored advices. The formulated policies were implemented as an advisor widget able to autonomously analyze usage and provide tailored energy feedback. The advisor is estimated leading to a potential of 34% of savings using measurement data from the GREEND dataset.

• 出版日期2017