This project aims to develop novel tools for energy disaggregation and monitoring of device health status. These tools will perform analysis of complex energy load time-series using real-time pattern recognition/matchmaking and hardware accelerated algorithms, and will transmit the recognised events to a main server.
During its first 20 months, the project has successfully achieved the milestones set in the proposal. Following the initial training of the Fellow -also involving the review of cutting edge methodologies and current trends-, a prototype was developed for High frequency sampling of energy data. Deployment of the prototype to a commercial building has so far provided over 1Tb of energy consumption and ground truth data.
1. Kotsilitis S., Marcoulaki E., Kalligeros E., Mousmoulas Y., 2018. Energy efficiency and predictive maintenance applications using smart energy measuring devices. In S. Haugen, A. Barros, C. van Gulijk, T. Kongsvik & J.E. Vinnem (eds.) “Safety and Reliability – Safe Societies in a Changing World”, CRC Press, ISBN 978-0-8153-8682, pp. 987-994, https://www.taylorfrancis.com/books/9781351174657
2. Kotsilitis S., Marcoulaki E., Kalligeros E. & Mousmoulas Y., 2018. Distributed edge computing paradigm with dedicated devices for energy efficiency and predictive maintenance applications. In “Industrial Internet of Things and Smart Manufacturing”, Springer Series on Lecture Notes on Data Engineering and Communications Technologies (NDECT), in press (ISBN: 978-1-912532-06-3)
3. Kotsilitis S., Marcoulaki E., Kalligeros E., 2019. High Frequency Energy Disaggregation Sampling and Analysis towards Predictive Maintenance Applications. In M. Beer & E. Zio (eds.) “Proceedings of the 29th European Safety and Reliability Conference”, Research Publishing, Singapore, pp. 1214-1222, ISBN: 978-981-11-2724-3; https://doi.org/10.3850/978-981-11-2724-3.