# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1905.05015