Author: Jan Siroky

Webinar recording: Energy Flexibility Explained – From Concept to Machine Learning in Practice

Post author By Jan Siroky
Post date June 4, 2025
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Energy Twin and the University Centre for Energy Efficient Buildings (UCEEB) of Czech Technical University in Prague explored the rising importance of load-side flexibility in today’s dynamic energy systems. With variable renewables on the rise, from solar in California to wind in Denmark and hydro in Scandinavia, adapting demand to real-time grid conditions is becoming a major competitive advantage.

This webinar covers the flexibility concept and shows how machine-learning-based predictions of building energy use enable smarter demand response, battery scheduling, and energy cost minimization. Real-world examples demonstrate how predictive models help integrate buildings into decentralised and fast-evolving energy grids.

The topics covered include:
Energy Flexibility: Markets, Challenges, and Opportunities – V. Zavřel, UCEEB

Overview of flexibility in global energy systems and growing demand-side potential
Impact of renewables and decentralisation on grid dynamics
Role of buildings in providing grid flexibility without ready-made solutions
Challenges and opportunities for commercial building operators

Predicting Building Energy Use for Flexibility – J. Široký, Energy Twin

How AI-driven forecasts enable smarter demand response and cost reduction
Case study: ML model peak demand analysis
Research insight: Community-driven flexibility challenges

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Webinar recording: How AI Enhances ESG & Cost Savings Goals in Commercial Buildings?

Post author By Jan Siroky
Post date March 28, 2025
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Energy Twin – Example of utilising Machine Learning in the Energy Sector

Post author By Jan Siroky
Post date May 9, 2022
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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.

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Evaluation of Covid 19 Impacts on Energy Consumption Using Machine Learning

Post author By Jan Siroky
Post date October 8, 2021
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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 the covid 19 pandemics.

Using the Energy Twin, we can evaluate average weekly profiles for each period, compensating for the effect of the different outdoor temperatures. The Energy Twin model predicts how the energy consumption would look like with real weather conditions. We then compare the difference between measured data and Energy Twin model prediction.