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.
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.
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.
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.
In the energy industry, professionals face challenges predicting and forecasting energy consumption. Energy Twin & Long-term Forecast Forecasting future energy consumption for extended periods, such
Using ChatGPT has become a huge trend. It is supposed to be able to make many professions more efficient – why not energy engineers? We
In recent years, there has been a significant increase in demand for EVs. However, there are numerous challenges associated with EV charging, such as multiple
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
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
SkyFoundry has published a new Energy Twin case study. In this case study, we focus on the electrical energy consumption of three shopping malls during
