Social behaviour of people is changing, we are permanently connected to Internet through our smartphones. People are demanding instant responses to their demands in different sectors and, at the same time, are providing highly valuable information that companies, that are receiving these information, could take advantage to provide them new added value services. It is very-well accepted in social networks, but it is not so deployed in the area of energy/water, and at the same time it would be very profitable for our green society. In this context, crowd-sensing is opening a new window to 21st century services.
Crowd-sensing is an emerging technology that allows improving observability of large spaces and their interaction with users and activities by exploiting smartphone sensing capabilities and aggregating conveniently. Crowd-sensing enables the observation of social behaviour at different aggregation levels but, it requires specific methods and adapt variability of data streams to existing energy monitoring solutions.
Thus, the goal of CROWDSAVING project is to provide a flexible and adaptive software infrastructure levering to monitor the impact of user behaviour on the dynamics of energy/water distribution networks and energy intensive facilities.
This general objective has been split into two subprojects addressing complementary objectives from two points of view, software architecture and energy monitoring. Subproject 1, led by UPM team, addresses a step forward in current software architecture design and mechanisms, to support dynamic and self-adaptable distribution networks. Subproject 2, led by UdG, focuses on new methods to monitor the impact of social behaviour on energy efficiency of power networks and facilities.
Integration of architecture and monitoring methods dealing with the requirements of crowd-sensing and allowing better observability of the impact of users’ behaviours on energy intensive spaces will be validated in two pilots at the UdG and UPM’s infrastructures. These pilots will be performed in collaboration with other agents that have demonstrated the interest for the results.
The project has been organised in six work packages dealing with WP1: Requirements definition, WP2: Architectural framework, WP3: Learning user/behaviour models based on crowd-sensing, WP4: Behavioural energy monitoring, WP5: Integration and validation (pilots), WP6: Coordination, Dissemination and Exploitation.
CROWDSAVING project is based on previous results of the consortium (UPM&UdG) achieved in the project MESC (DPI2013-47450-C2-R, 2014-16) through the integration of crowd-sensing with information traditionally managed in energy management systems (BMS/BEMS, WSN, Smart meters, weather station, etc.). As result, CROWDSAVING will take an important step forward in the area allowing a standardised access to energy and crowd-sensing data