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Open Access Publications from the University of California

Predicting success of energy savings interventions and industry type using smart meter and retrofit data from thousands of non-residential buildings


This paper discusses the creation of targeting and segmentation information about non-residential buildings that are equipped with advanced metering infrastructure (AMI) meters, or smart meters. Statistics, model, and pattern-based temporal features are extracted from over 36,000 smart meters. They are then merged with a database of past energy efficiency interventions such as lighting, HVAC, and controls retrof its from 1,600 buildings. The buildings are divided into Good, Average, and Poor performing classes according to consumption from before and after the retrofits. Classification models are developed that improve the ability to predict retrofit success and standard industry class by 18.3% and 27.6% respectively over baselines. This study serves as an example of better leveraging smart meter data from non-residential buildings for utility targeted incentive programs. The methodology outlined is preliminary and further models and temporal features are to be tested.

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