Energy Twin can analyse and predict data on many buildings in parallel, saving a lot of time for ESCO experts.
With its precise analysis model, Energy Twin reveals new potential for reducing consumption and dramatic cost savings.
These case studies accurately illustrate 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.
Our objective is to improve energy efficiency through state-of-the-art tools and algorithms. We have over 10 years of experience with mathematical modeling of energy consumption and various HVAC appliances. Our team consists of 5 professionals who will provide you reliable support for the Energy Twin. Moreover, we can also offer the development of a custom machine learning solution, if needed.
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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
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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
