Beyond - Virtual Reality enabled energy services for smart energy systems

Initial situation

The Austrian government is committed to accelerating the transition of its energy systems and achieving CO₂ neutrality by 2040. To achieve this goal, Austria must significantly step up its efforts to decarbonise all parts of its energy sector. Buildings account for about a third of the total end-use energy demand. Europe is now entering the fourth wave of energy efficiency as characterised by the increased digitization of society, distributed energy resource deployment, and the changing nature of the energy supply and demand mix. Intelligent energy services such as predictive maintenance, demand-side management and model predictive control are central components for reducing the energy consumption of buildings and transforming buildings into intelligent actors in the next generation of smart energy systems. Demand-side adoption of IoT technologies in homes, commercial and industrial buildings, as well as educational and community facilities offers enormous potential to increase efficiency through more comprehensive energy management systems.

The current landscape of engineering education reveals a noticeable gap, hindering students from acquiring immersive and practical components crucial for a comprehensive understanding of complex concepts related to building physics. Simultaneously, predictive maintenance processes in building systems encounter challenges in terms of efficiency and accuracy, underscoring the necessity for innovative solutions. Recognising these gaps sets the stage for the "Beyond" project to address them effectively.

The "Beyond" project aimed to develop the technological foundation for "Next Generation Energy Services", which is made possible by the interplay of the following technologies: 

• Virtual Reality (VR) for visualization and real-time interaction with the real building; 
• Machine learning and physical simulation to show the real-life effects of interventions and decisions; 
• Internet of Things (IoT) platforms for the realisation of smart systems and bidirectional real-time communication between the building and its users. 

The technological developments were tested and evaluated on two use cases of energy services "Human Aspects in Buildings" and "Predictive Maintenance and Error Diagnosis".

Objectives

The first use case, Energy Service 1 "Human Aspects in Buildings," aimed to seamlessly integrate theoretical knowledge and practical skills, especially within the domain of building physics, and to facilitate an understanding of the impact of user decisions on the performance of buildings. This use case sought to foster a hands-on educational experience through virtual reality, contributing to a deeper understanding of practical applications through an educational VR game. The methodical approach employed in use case 1 utilised cutting-edge VR technology coupled with the IoT platform, energy simulation, and machine learning (ML) to evaluate users' changes in real-time. Use case 1, designed as an educational VR game, provides an innovative and immersive learning environment tailored for engineering students. It seamlessly integrates practical scenarios and data visualization, elevating their comprehension of intricate concepts. The results showcase an interactive VR environment, enriched with visual, haptic, and auditory feedback, delivering a remarkably realistic virtual experience. Engaging in practical scenarios within the game, VR users cultivate a profound understanding of sustainable and energy-efficient building design, further enhancing their skills and knowledge in the field.

The second use case, Energy Service 2 "Predictive Maintenance and Automatic Fault Detection" focused on real-world applications, seeking to enhance the efficiency of monitoring and maintaining air filters in buildings' Heating, Ventilation, and Air Conditioning (HVAC) systems through predictive maintenance and automatic fault detection. The project strived to optimise building maintenance practices by leveraging advanced data analytics, machine learning (ML) and Internet of Things (IoT) technologies to predict and detect potential faults in HVAC systems. This approach emphasised the iterative process of refining predictive maintenance strategies based on an increased dataset, staying abreast of technological advancements. Despite challenges in predicting exact air filter lifespan, the machine learning models exceled and showed promise in forecasting changes in differential pressure, providing valuable insights for HVAC system maintenance. Although further data accumulation and technological advancements are recognised as integral for optimisation, the project findings contribute significantly to the field of predictive maintenance of HVAC systems.

Outlook

Looking ahead, the project envisions several future developments to enhance the impact of use case 1. These include introducing multiplayer experiences in the educational VR game for collaborative learning and refining predictive maintenance strategies through advancements in sensor technologies and data analytics. The project's outcomes target diverse audiences, including students (e.g. from civil engineering, architecture and mechanical engineering), building industry professionals, and the broader research community, with the ultimate aim of advancing sustainable building practices and innovative educational methodologies.

The outlook for use case 2 is promising in real-world applications, particularly in predictive maintenance and automatic fault detection in air filters of HVAC systems. Nevertheless, further data accumulation is suggested for the refinement of predictive maintenance strategies for HVAC systems, and additional work is needed for the development of accurate models to determine the residual useful life of air filters.

Project management

Institute of Building Physics, Services and Construction (IBPSC), Graz University of Technology

Project or cooperation partners

• Institute of Interactive Systems and Data Science (ISDS), Graz University of Technology
• Institute of Software Technology (IST), Graz University of Technology
• EAM Systems
• EnaLytics

TU-Graz
Institute for Building Physics, Building Technology and Building Construction
Univ.-Prof.Dipl.-Ing.Dr. Christina J. Hopfe
Lessingstraße 25/III
A-8010 Graz
Tel.: +43 316 873 - 6240
E-mail: c.j.hopfe@tugraz.at
Web: www.tugraz.at/en/institutes/ibpsc/