Energy Twin – Using ML to prevent exceeding the contracted daily maximum gas consumption

In our previous post, we discussed how to manage an extensive building portfolio using Energy Twin and machine learning. This time we will show you how it can be further applied to another real-world example.

Energy Twin models can be used to forecast the future energy consumption profile, from a few hours to a few days ahead. This prediction was used as part of the control system for a virtual power plant’s Combined Heat and Power (CHP).

The virtual power plant’s goal is to use CHP and sell electrical energy when it is beneficial. In this case, heat is generated by both gas boilers and CHPs; however, CHPs have a lower gas-to-heat efficiency than gas boilers. As a result, the operator must prioritise gas boilers even if it contradicts the virtual power plant’s trading plan. Exceeding the contracted daily maximum gas consumption is far more expensive than the discrepancy in electrical energy delivery.

To avoid costly penalties, the model predicts daily gas consumption and is updated every hour. If a risk of exceeding occurs, the operator receives a notification with detailed information and suggested actions so that local technicians can respond in advance, if needed.


Energy Twin – Example of utilising Machine Learning in the Energy Sector

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.