Predictive building management system for improved energy ef ciency in smart buildings Columbia University in the City of New York
New York, New York
OVERVIEW This technology is a building management system that collects and uses historical and real-time data to provide reliable predictions of energy consumption and accurate recommendations for ef cient energy management.
HIGHLIGHTS
Inventors
Roger N Anderson Albert G Boulanger
Unmet Need: Integrative building management system Building management systems are used to monitor and control energy demand from mechanical and electrical systems. With the increasing prevalence of smart buildings, there is a growing need for more sophisticated building management systems that can integrate data from multiple systems and sources, including energy consumption by various building operational systems, tenant occupancy patterns, and even external conditions such as weather. Improved techniques for building energy management can increase the energy ef ciency, resiliency and reliability of building operations and management while ensuring tenant comfort.
Leon Li Wu Arthur Kressner Vaibhav Bhandari Ashish Gagneja Ashwath Rajan Jessica Forde John Gilbert Vivek Rathod
Tags
Algorithm Machine Learning
The Technology: Predictive algorithm for managing building energy supply and demand This technology is a building management system that integrates building data from internal and external systems to predict energy supply and demand. A machine learning predictive model is used to generate energy demand forecasts and automated analysis that can guide optimization of building operations to improve tenant comfort while improving energy ef ciency. The system provides actionable recommendations that can help to reduce energy waste and increase cost ef ciency for building operations.
Waste Minimisation Support Vector Machine Contact Information Richard Nguyen
[email protected]
Resources
View at our website
Applications: Multi-unit residential buildings Commercial of ce buildings Energy consumption prediction Energy waste reduction
Advantages: Integrates data from a wide range of sources and systems for more accurate prediction Columbia Technology Ventures
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Compatible with existing building management systems Reacts in real-time to the system needs of energy suppliers
Lead Inventor: Roger Anderson, Ph.D.
Patent Information: Patent Pending (US 20150178865) Patent Pending (US 20140249876)
Related Publications: Solomon DM, Winter RL, Boulanger AG, Anderson RN, Wu LL. “Forecasting Energy Demand in Large Commercial buildings Using Support Vector Machine Regression” Columbia University Computer Science Technical Reports. 2011. Wu LL, Kaiser GE, Solomon DM, Winter RL, Boulanger AG, Anderson RN. “Improving Ef ciency and Reliability of Building Systems Using Machine Learning and Automated Online Evaluation” Columbia University Computer Science Technical Reports. 2012.
Tech Ventures Reference: IR CU12084-a Licensing Contact: Richard Nguyen
Columbia Technology Ventures
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