Introduction

Buildings account for 40% of global energy use, with HVAC systems as the largest energy consumer. Most traditional building management systems operate on inefficient binary controls, heating or cooling entire areas regardless of occupancy. Aventus AI’s innovative AI-driven solutions enable real-time optimisation of HVAC operations, reducing energy waste and carbon emissions while improving comfort and cost efficiency.

Discover how Aventus AI’s digital twin solutions optimise building management—Click here to learn more.

Industry Application

• Sector: Building Management Systems and Smart Building Technology
• Use Case: AI-driven HVAC optimisation to reduce operational costs, improve energy efficiency, and meet sustainability goals.

The Challenge

• Traditional BMS systems lack granularity, leading to energy waste through “all-on” or “all-off” heating or cooling.
• Limited IoT sensor infrastructure and no room-level control mechanisms, such as actuated valves, prevent effective optimisation.
• Rising energy costs and sustainability pressures demand innovative solutions to manage HVAC systems dynamically.

Global Market Opportunity

• The global smart building market is valued at $80 billion and is expected to exceed $120 billion by 2030.
• Sustainability initiatives and regulatory mandates are driving demand for AI-integrated building management systems.

Solution

Aventus AI and Ecosystem Partners Deliver:

1. Digital Twin Integration: A real-time digital twin to simulate and optimise HVAC operations.
2. Occupancy-Based Control: Recommendations for integrating actuated valves and IoT sensors to enable room-level heating and cooling.
3. AI-Driven Optimisation: Machine learning models dynamically adjust temperature setpoints based on occupancy and environmental factors.
4. Energy Efficiency: Demonstrated energy savings by reducing ambient temperatures from 23°C to 19°C.

Execution Plan

1. Assessment Phase:
   1. Conduct an energy audit to identify inefficiencies and areas for improvement.
2. System Design:
   1. Develop custom digital twins and train predictive AI models.
3. Implementation:
   1. Retrofit HVAC systems with actuated valves, IoT sensors, and occupancy counters.
4. Optimization:
   1. Continuously refine HVAC schedules and settings using real-time data and predictive analytics.

Return on Investment (ROI)

• Energy Savings: 30% reduction in HVAC energy costs.
• Carbon Reduction: 25% decrease in carbon emissions.
• Scalability: Optimised room-level control for diverse building environments, lowering operating costs and increasing tenant satisfaction.

Regulatory Standards

Incorporating global standards into smart building systems:

• Cybersecurity (IEC 62443):
  • Secures IoT-connected HVAC systems, protecting sensitive building data and ensuring uninterrupted operation of building management systems.
• Information Management (BIM ISO 19650):
  • Maintains structured IoT data and digital twin integration for consistent asset management across the building lifecycle.
• ISO 50001: Energy management system compliance.
• LEED and BREEAM: Supports green building certification.
• ASHRAE Standards: Enhanced HVAC performance efficiency.

Added Value: Clients benefit from robust cybersecurity and lifecycle asset management, ensuring long-term sustainability and operational excellence.

Outcome

• Environmental Impact: Reduced energy consumption and carbon footprint.
• Economic Value: Significant operational cost savings and increased ROI for building owners.
• Scalable Solution: Demonstrated ability to retrofit standard BMS systems globally, unlocking untapped energy efficiency potential.