Ontology-Based Data Representation Prototype for Indoor Air Quality, Building Energy Performance, and Health Data Computation

Abstract

This study investigates the data integration of IoT-enabled sensor networks, emphasizing energy performance and personalized indoor air quality (IAQ) solutions to improve indoor environments, energy efficiency, and sustainability. Ontologies—structured frameworks that standardize data representation and enable interoperability—are the tools for interpreting complex IAQ data for optimal energy rate plans and health situations. Our methodology follows the well-established three-phase engineering approach. We present the design of a prototype with essential classes, which is proposed to integrate IAQ data with health conditions, enhancing real-time monitoring and automated decision making for optimal energy performance for smart buildings. Our research goal is to define the most essential classes, arranging them hierarchically to create a prototype for data computing covering IAQ, energy performance, and health aspects. This ontological framework, covering all three aspects, addresses a current research gap. Results demonstrate the minimum viable product with 78 classes for a smart home IoT system, providing tailored indoor climate control based on user health profiles and energy performance. This prototype represents a significant advancement in sustainable building and IAQ management, promising improved building energy performance, occupant health, and comfort. Future research will validate this framework through extensive testing in real-world environments.