A resource-based rule engine for energy savings recommendations in educational buildings

TL;DR

This paper introduces a resource-based rule engine that uses IoT data and a graph model to generate energy-saving recommendations in educational buildings, aiming to enhance awareness and promote energy efficiency.

Contribution

It presents a novel, configurable rule engine leveraging a resource-based graph model for personalized energy savings recommendations in school buildings.

Findings

Engine supports customization for different buildings

Preliminary results show effective recommendation generation

Design emphasizes ease-of-use and extensibility

Abstract

Raising awareness among young people on the relevance of behaviour change for achieving energy savings is widely considered as a key approach towards long-term and cost-effective energy efficiency policies. The GAIA Project aims to deliver a comprehensive solution for both increasing awareness on energy efficiency and achieving energy savings in school buildings. In this framework, we present a novel rule engine that, leveraging a resource-based graph model encoding relevant application domain knowledge, accesses IoT data for producing energy savings recommendations. The engine supports configurability, extensibility and ease-of-use requirements, to be easily applied and customized to different buildings. The paper introduces the main design and implementation details and presents a set of preliminary performance results.