#6004 Real World Application of Machine Learning in SkySpark - Energy Twin: Monitoring Based Commissioning

Machine Learning and other AI-based techniques continue to gain attention as they are applied to real world challenges. In the field of analytics in the built environment, SkySpark has employed a range of AI techniques since its introduction – well before the terms were hot. The foundation of SkySpark is its pattern recognition capability and technology to manage and interpret “graph” relationships between data, devices and equipment systems.

Real world examples are the best way to highlight how these technologies actually deliver value to facility operators and we are excited to publish a case study, Energy Twin: SkySpark-based Machine Learning for Monitoring-Based Commissioning. Developed and implemented fully in SkySpark, Energy Twin, is a machine learning application for energy consumption analysis that reveals areas for energy use reductions and system optimization.

In this case study, the Energy Twin team, a SkyFoundry partner, showcases their work in applying these advanced techniques to monitoring based commissioning. Access the Energy Twin case study here