Muehleisen et al., 2016 - Google Patents
Bayesian calibration-what, why and howMuehleisen et al., 2016
View PDF- Document ID
- 15687956410401569790
- Author
- Muehleisen R
- Bergerson J
- Publication year
External Links
Snippet
Calibration of building energy models is important to ensure accurate modeling of existing buildings. Typically this calibration is done manually by modeling experts, which can be both expensive and time consuming. Â Additionally, biases of the individual modelers will creep …
- 238000000034 method 0 abstract description 21
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Muehleisen et al. | Bayesian calibration-what, why and how | |
US10739741B2 (en) | Systems and methods for detecting changes in energy usage in a building | |
Sun et al. | Calibration of building energy simulation programs using the analytic optimization approach (RP-1051) | |
US10261485B2 (en) | Systems and methods for detecting changes in energy usage in a building | |
US9575475B2 (en) | Systems and methods for generating an energy usage model for a building | |
Chong et al. | Guidelines for the Bayesian calibration of building energy models | |
Martínez et al. | A performance comparison of multi-objective optimization-based approaches for calibrating white-box building energy models | |
Rivalin et al. | A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings | |
Capozzoli et al. | Estimation models of heating energy consumption in schools for local authorities planning | |
Li et al. | Assessment of linear emulators in lightweight Bayesian calibration of dynamic building energy models for parameter estimation and performance prediction | |
Hester et al. | Actionable insights with less data: guiding early building design decisions with streamlined probabilistic life cycle assessment | |
Yang et al. | An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study | |
Singh et al. | Quick energy prediction and comparison of options at the early design stage | |
Coakley et al. | Calibration of a detailed BES model to measured data using an evidence-based analytical optimisation approach | |
Heo et al. | Evaluation of calibration efficacy under different levels of uncertainty | |
US20100057416A1 (en) | Method and system for estimating building performance | |
US11269303B2 (en) | Systems and methods for detecting changes in energy usage in a building | |
Mai et al. | Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data | |
De Coninck et al. | Grey-box building models for model order reduction and control | |
Lim et al. | Estimating unknown parameters of a building stock using a stochastic-deterministic-coupled approach | |
Wang et al. | The influence and adjust method of hyperparameters’ prior distributions in Bayesian calibration for building stock energy prediction | |
Ekström et al. | Probabilistic risk analysis and building performance simulations–Building design optimisation and quantifying stakeholder consequences | |
Papadopoulou et al. | Evaluating predictive performance of sensor configurations in wind studies around buildings | |
Uddin et al. | Sensitivity analysis for identifying key parameters affecting energy consumption in early-stage building design | |
Reynders et al. | Impact of the heat emission system on the identification of grey-box models for residential buildings |