In today’s rapidly advancing world, artificial intelligence (AI) and machine learning (ML) are creating powerful new ways to analyse data and unlock energy savings that were previously out of reach. 

Digitalisation and AI are now essential in building management, especially for optimising energy use. With climate change and rising energy costs intensifying the need for efficient solutions, Optimise-AI combines machine learning with augmented semantics to offer a groundbreaking approach to energy management. Here’s how we’re redefining energy efficiency and sustainability across the built environment. 

The Challenge 

Buildings account for nearly 40% of global energy use. In particular, non-domestic buildings, many of which are not actively managed or lack updated Building Management Systems (BMS), often have inefficient heating, cooling, and lighting systems. This leads to excessive energy waste and inflated costs. Adding to the complexity, 90% of older buildings lack historical datasets and BMS systems, making data-driven insights hard to obtain. 

Optimise-AI addresses this gap through AI-powered semantic digital twins, which provide predictive and adaptive management capabilities for these buildings. Using data-scarce solutions, our technology offers actionable insights even for buildings without traditional BMS infrastructure, ensuring immediate impact and access to energy efficiency. 

About Optimise-AI 

As an award-winning innovator in energy technology, Optimise-AI specialises in advanced, rapid-energy interventions for building assets, including those under data constraints. We leverage a patented methodology that combines digital twin technology with generative AI, resulting in semantic digital twins—models that don’t just mirror a building’s systems but truly understand and predict them. This innovation allows for an in-depth, actionable understanding of each building’s dynamics, empowering those who manage the buildings make better-informed decisions quickly. 

How It Works: Our Technology 

Optimise-AI’s semantic digital twins use cutting-edge ML and ontology-based language models to process complex building data with richer context. By integrating both historical and real-time information, these digital twins allow users to ask more insightful questions and receive precise answers, transforming passive data into active intelligence. Our technology is built on: 

1. Fast Machine Learning – Quickly analyses patterns and trends to create accurate energy models for each building. 

2. Federated AI and Co-Learning – Uses adaptable AI to interpret and learn from limited or no data, sharing insights across non-domestic buildings to maximise energy optimisation. 

3. Neural Networks and Genetic Algorithms – Captures nonlinear relationships between variables and finds optimal solutions across multiple dimensions, making it especially effective in optimising energy systems in complex building ecosystems. 

Real-World Impact 

Our Predict and Optimise tools provide those who manage non-domestic buildings with powerful, user-friendly insights for reducing energy consumption: 

• ‘Predict’ Capable of predicting future energy pathways and identifying opportunities for savings. 

• ‘Optimise’ Enhancing systems based on real-time energy demands, creating a dynamic response to changing conditions. 

With these tools in action, Optimise-AI has demonstrated up to 40% energy and cost savings by adjusting systems such as heating, cooling, lighting, and building fabric. This holistic approach captures each building's unique needs and offers tailored recommendations that are accessible via an intuitive dashboard. 

Conclusion: A Smarter, More Efficient Future 

Optimise-AI’s use of semantic digital twins and advanced AI provides a unique solution for non-domestic buildings, addressing today’s energy challenges with innovative efficiency. Our technology empowers building managers to achieve unprecedented levels of energy savings, lower carbon emissions, and create a path toward net-zero buildings. By embracing the power of AI-driven insights, we can unlock new levels of energy management and forge a sustainable future for building operations. 

For more information on Optimise-AI’s groundbreaking energy management solutions, visit optimise-ai.com. 

• References: [1] Rezgui, Y., Beach, T. H. O., Petri, I., Hippolyte, J. L., Ahmad, M. W., Wanqing, Z. H. A. O., & Howell, S. (2023). U.S. Patent No. 11,669,059. Washington, DC: U.S. Patent and Trademark Office, https://patents.google.com/patent/US11669059B2/en    

• Petri, I., Rezgui, Y., Ghoroghi, A. and Alzahrani, A. 2023. Digital twins for performance management in the built environment. Journal of Industrial Information Integration 33, article number: 100445. (10.1016/j.jii.2023.100445)  

• Petri, I., Li, H., Rezgui, Y., Chunfeng, Y., Yuce, B. and Jayan, B. 2014. A modular optimisation model for reducing energy consumption in large scale building facilities. Renewable and Sustainable Energy Reviews 38, pp. 990-1002. (10.1016/j.rser.2014.07.044)