EnergyTwin Case Studies

Energy Twin is a machine learning solution for energy consumption analysis and savings.

Finding black sheep in your building portfolio using Energy Twin KPIs

This case study accurately illustrates the possible practical benefits of machine learning for HVAC operation. Thanks to ET, a significant potential for energy savings was found and fully exploited during subsequent modifications of the building management system resulting in successful savings in energy consumption and building operation costs.

Energy Twin for Monitoring Based Commissioning

This case study is a good example of how AI can improve our work. In this case, AI does not replace an expert; it just makes their work more efficient. AI performs the repetitive and dull part of the job – such as comparing all measured data and detecting anomalies. The expert then spends precious time only with the events that matter and are worth investigating.

Evaluation of Covid 19 Impacts on Energy Consumption Using Energy Twin Machine Learning in SkySpark

During the pandemic, buildings were exposed to nonstandard regimes (reduced number of occupants, nonstop ventilation, total lockdown, etc.). Valuable data
were measured by Building Management Systems.
Analysis of these data provide valuable knowledge about the effectiveness of setback regimes.

From Crisis to Savings: Using Machine Learning to Verify Cost Savings Achieved With Energy Conservation Measures

This case study demonstrates the potential of energy-saving measures to achieve significant cost savings and reduction of energy consumption, even in challenging market environments. Immediate impact evaluation tools, such as Energy Twin Interactive and SkySpark, can enhance the efficiency of implementing such measures. These tools provide prompt feedback for fine-tuning and adjusting measures, while automating mundane tasks, thus reducing the workload of energy experts.

What our clients say