Let us show you how Energy Twin utilises machine learning in a real-world example.
Take an extensive building portfolio. Human experts cannot oversee hundreds of key measurements in real-time. How can we efficiently monitor electrical energy consumption and detect anomalies?
The answer is – machine learning. Energy Twin analysed the electrical energy meter of more than 150 buildings. A comprehensive overview of anomalies across the entire portfolio can be provided within minutes with identified candidates for detailed inspection using models of prediction and measured data.
This way, experts spend their valuable time only on the problematic buildings and leave the tedious work of monitoring all of the buildings to Energy Twin.
Detected anomalies were caused by various reasons – from local heaters with no setback to air doors manual regime override or wrong setup of an electrical meter. Such faults can remain undetected without using machine learning, resulting in unnecessary building operation costs.
