Data Aggregation, Fusion and Recommendations for Strengthening Citizens Energy-aware Behavioural Profiles

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.