U.S. Department of Energy

Pacific Northwest National Laboratory

Bayesian Support Vector Machine (BSVM)

  • Product Image:


A modified nonlinear Bayesian support vector machine (BSVM).
Technologies are needed for effectively managing building energy performance, especially in the commercial buildings space. Faults and equipment degradation can significantly increase energy consumption. Tools for fault detection and diagnostics (FDD) are needed. Most commercial buildings lack sophisticated FDD tools. The FDD tools that are gaining traction in practice are rule-based and hence, the set of faults detected and diagnosed by these tools are limited to those that are predefined. Machine learning based approaches are purely based on extracting features from historical data. They can include, but it is not necessary, assumptions from physics-based building models and expert knowledge. Therefore, a machine learning based approach will enable us to discover new conditions that have not been seen previously, which may indicate the existence of faults or equipment degradation. The BSVM has been modified to use a single class of data —a “one-class” BSVM. Traditionally, SVMs work by learning a separating line (hyperplane) between two classes of data that is maximally separated from both classes. When new data is tested, whichever side of the line it falls on determines the predicted class. The one-class BSVM instead learns the (nonlinear) boundary around the single class of training data. At test time, new data is determined to be an inlier or an outlier based on which side of the boundary it falls.


Produced under funding from the U.S. Department of Energy


This material was prepared as an account of work sponsored by an agency of the United States Government.  Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights.

Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORY operated by BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY under Contract DE-AC05-76RL01830.


You must login to add products to cart.
You must login to add products to wishlist
You must be verified to add products to cart.
You must be verified to add products to wishlist.
| Pacific Northwest National Laboratory