Data Aggregation, Fusion and Recommendations for Strengthening Citizens Energy-aware Behavioural Profiles
Eleni Fotopoulou, Anastasios Zafeiropoulos, Fernando Terroso, Aurora, Gonzalez, Antonio Skarmeta, Umutcan \c{S}im\c{s}ek, Anna Fensel

TL;DR
The paper presents the ENTROPY platform, an integrated system that uses sensor data, semantic web, and machine learning to promote energy-efficient behaviors among building occupants.
Contribution
It introduces a comprehensive platform combining novel sensor networking, data integration, and personalized recommendations to enhance energy awareness and behavioral change.
Findings
Effective sensor data aggregation mechanisms implemented
Semantic web technologies enable unified data representation
Machine learning provides actionable insights for personalized recommendations
Abstract
In this paper, ENTROPY platform, an IT ecosystem for supporting energy efficiency in buildings through behavioural change of the occupants is provided. The ENTROPY platform targets at providing a set of mechanisms for accelerating the adoption of energy efficient practices through the increase of the energy awareness and energy saving potential of the occupants. The platform takes advantage of novel sensor networking technologies for supporting efficient sensor data aggregation mechanisms, semantic web technologies for unified data representation, machine learning mechanisms for getting insights from the available data and recommendation mechanisms for providing personalised content to end users. These technologies are combined and provided through an integrated platform, targeting at leading to occupants' behavioural change with regards to their energy consumption profiles.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
