Machine learning software for optimization of building energy usage

This technology is a machine learning algorithm which uses historical and real-time operational building data to predict building energy demands.

Unmet Need: Prevention of large-scale, excess energy consumption

Current methods to implement energy efficiency in commercial and residential buildings can be designed for tenant comfort, energy efficiency and system reliability in mind with the use of energy-efficient materials and building management systems (BMSs). However, these BMSs do not always guarantee reliable building operation because they do not measure or provide visibility and data analytics of space temperatures or occupancy variations sufficiently, leading to buildings often consuming energy at levels that exceed design specifications. As a result, there is a current need to implement systems capable of optimizing building management and improving energy efficiency.

The Technology: Machine learning algorithm for monitoring buildings and optimizing energy use

This technology consists of a machine learning algorithm which incorporates both past and real-time operational building data, such as humidity and temperature, to predict building energy demands. Historical temperature and humidity readings are used to train the algorithm, which then makes future predictions and recommends operation actions. Using an automated online evaluator, predictions are compared to real-time data to further refine the algorithm, detect anomalies, and ensure reliability. This technology allows for optimization of overall building energy use and can be applied in both commercial and residential buildings to reduce energy costs.

This technology has been validated in a multi-tenant Manhattan office building for both spring and winter seasons.

Applications:

• Prediction and optimization of energy consumption at single-building or district-scale
• Optimizing commercial or residential building climate control
• Optimization of other building parameters such as lighting
• Monitoring thermostats and other building devices for failure

Advantages:

• Utilizes machine learning to constantly update the algorithm and increase accuracy
• Uses a high-dimensional space to train the algorithm
• Combines both real-time data and historical data points to correct for prediction errors
• Uses an automated online evaluator for anomaly detection and to ensure algorithm reliability
• Cost-effective

Lead Inventor:

Roger Anderson, Ph.D.

Patent Information:

Patent Status

Related Publications:

• Wu L, Kaiser G, Solomon D, Winter R, Boulanger A, Anderson R. “Improving efficiency and reliability of building systems using machine learning and automated online evaluation” 2012 IEEE Long Island Systems, Applications and Technology Conference (LISAT). 2012; 1-6.

• Solomon DM, Winter RL, Boulanger AG, Anderson RN, Wu LL. “Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression” Columbia University Academic Commons. 2011.

Tech Ventures Reference:

• IR CU12298

• Licensing Contact: Richard Nguyen