Machine learning is often discussed as a future promise in energy management—but how does it deliver value at scale, using real operational data? In this joint webinar, Energy Twin and SkyFoundry present a practical approach to applying Python-based machine learning with SkySpark® to analyze and optimize large building portfolios.
Using a real banking portfolio with 60+ branches as a case study, we demonstrate how individual ML models can be built for each building to enable:
Continuous anomaly detection and Monitoring-Based Commissioning (MBCx)
Identification of small but persistent performance deviations
ML-based benchmarking across portfolios
Estimation of building-level and portfolio-wide energy saving potential
Using only 15-minute main meter data, this approach delivered over 5% portfolio-wide energy savings during the first year of operation, with clear site prioritization and transparent verification.
The webinar demonstrates how SkySpark provides a scalable and flexible foundation for advanced ML workflows, and how combining it with domain-specific energy expertise turns raw data into actionable, verifiable energy insights.
Debbie Bretches Thu 18 Dec
Join us for a SkyFoundry and Energy Twin webinar, Small Data, Big Impact: How Main Meter Data Delivered 5% Energy Savings Across 60+ Buildings .
Machine learning is often discussed as a future promise in energy management—but how does it deliver value at scale, using real operational data? In this joint webinar, Energy Twin and SkyFoundry present a practical approach to applying Python-based machine learning with SkySpark® to analyze and optimize large building portfolios.
Using a real banking portfolio with 60+ branches as a case study, we demonstrate how individual ML models can be built for each building to enable:
Using only 15-minute main meter data, this approach delivered over 5% portfolio-wide energy savings during the first year of operation, with clear site prioritization and transparent verification.
The webinar demonstrates how SkySpark provides a scalable and flexible foundation for advanced ML workflows, and how combining it with domain-specific energy expertise turns raw data into actionable, verifiable energy insights.
Register today at: https://bit.ly/4pKgGLt