A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach
Highlights We present a Gaussian process modeling methodology that handles uncertain sensor data. The methodology yields robust predictions and confidence levels. The methodology can reduce data requirements for M&V applications.
Abstract Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M&V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M&V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.
Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach
Highlights We present a Gaussian process modeling methodology that handles uncertain sensor data. The methodology yields robust predictions and confidence levels. The methodology can reduce data requirements for M&V applications.
Abstract Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M&V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M&V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.
Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach
Burkhart, Michael C. (author) / Heo, Yeonsook (author) / Zavala, Victor M. (author)
Energy and Buildings ; 75 ; 189-198
2014-01-30
10 pages
Article (Journal)
Electronic Resource
English
Gaussian process modeling for measurement and verification of building energy savings
| Online Contents | 2012