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.
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.
Energocentrum has been operating on the market for over 20 years and is an established player in energy services. The Energy Twin team leverages its background, team and experience to innovate the energy sector with AI.
SkyFoundry’s software solutions are designed to help clients derive value from their investments in smart systems. We use the SkySpark platform and have developed an AI analysis and prediction plugin for SkySpark users.
AI for Main Electricity Meter Analysis: The Basics Artificial Intelligence (AI) is revolutionizing energy analysis by turning granular smart meter data into actionable insights. It
It’s always good practice to double-check your input data before diving into analysis. Sometimes you’ll catch small inconsistencies — and sometimes, you’ll uncover something more
Author Jan Siroky View all posts
At Energy Twin, we’ve been working on a research project exploring how machine learning can be used for general fault detection. Like any good research,
Companies often understand the importance of deploying smart meters and getting data online. They see it as a key milestone—and while it is, it’s only
Detecting anomalies in energy data is key to optimizing consumption, reducing costs, and ensuring building systems run efficiently. With the vast amount of data from
