- freely available
Energies 2015, 8(4), 2548-2574; https://doi.org/10.3390/en8042548
2. Typical Calibration Issues
|Calibration Levels||Building Input Data Available|
|Utility Bills||As-Built Data||Site Visit or Inspection||Detailed Audit||Short-Term Monitoring||Long-Term Monitoring|
- Standardization. Statistical criteria are used for assessing whether or not a building model can be considered calibrated. They do not provide a method about how calibrating a building model. Therefore, so far, there is no formal and recognized standard methodology or guidelines for CS, which is usually carried out based on users’ judgment and experience.
- Calibration costs. The modeling process does not represent an easy task, even for building simulation that does not require calibration. Calibrated models are far more complicated and require higher expenses than “uncalibrated” models. Calibration, as no automated procedure has been defined yet, is highly time-consuming indeed. Furthermore time and expense for collecting sub-metered data, contribute to CS costs.
- Model complexity. Depending on the type of energy model created and on the model complexity, the number of input data considered may vary. Normative quasi-steady models are simpler than transient energy models, created within energy simulation program (e.g., EnergyPlus, TRNSYS (Transient System Simulation Tool), etc.). The degree of simplification of the building model concerns directly the input data, as the more complex the models is, the larger amount of input data are required.
- Model input data. Large quantity of input data are always involved in the building modeling process. However, the quantity may vary depending on the level of detail pursued in the model definition and on the data availability (e.g., problems of data quality). Measured data are sometime used for providing the model with further information (e.g., building occupancy, temperature set point, etc.) during validation of the calibrated model based on statistical indices.
- Uncertainty in building models. When manual calibration is carried out, a deterministic approach is usually adopted. However as not all input data affect the investigated energy consumption in the same ways, it is important to identify, throughout a screening analysis, the parameters that influence the most the building model, and define their level of uncertainty.
- Discrepancies identification. Issues concerning the reason of discrepancies between simulated consumption and measured consumption is often encountered during CS. Experienced users may be able to detect the underlying causes of the mismatch due to their building simulation skills and knowledge. These disagreements may be linked to a chain of causes or imputation errors in building model definition or also to measurements errors.
- Automation. So far, no approved automated methodology for calibration has been presented. Various CS application, based on users’ experience and manual approach, can be listed. An automated methodology will so far reduce expenses and also attempt to wider the knowledge of calibration to other professionals.
- User’s experience. Another issue that should be taken into consideration is the user’s experience. Reddy et al.  claims that “calibration is highly dependent on the personal judgment of the analyst doing the calibration”. Since from the first stages of simulation, the user’s experience can affect calibration results. Even with a systematic and automated procedure, users are still responsible of CS and a more than basic knowledge of the building simulation domain is required for applying the procedure. A deep sensibility towards the modeling process may in fact reduce calibration expenses, in terms of timing and avoiding mistakes.
3. Criteria for the Model Goodness-of-Fit
- M is the measured energy data point during the time interval; and
- S is the simulated energy data point during the same time interval.
|Statistical Indices||Monthly Calibration||Hourly Calibration|
|St. 14||IPMVP||FEMP||St. 14||IPMVP||FEMP|
4. Calibration Methodologies for Building Simulation Models
- manual calibration methods based on an iterative approach;
- graphical-based calibration methods;
- calibration based on special tests and analysis procedures; and
- automated techniques for calibration, based on analytical and mathematical approaches.
4.1. Manual Calibration
4.2. Graphical Techniques
- 3D comparative plots; and
- calibration and Characteristic signature.
4.2.1. 3D Comparative Plots
4.2.2. Calibration Signature
- subscripts HTG and CLG refer, respectively, to the heating and cooling time intervals considered;
- RSME is the Root Mean Squared Error calculated as in Equation (3); and
- MBE is the Mean Bias Error calculated as in compliance with Equation (1).
4.3. Calibration Based on Analytical Procedures
4.4. Automated Techniques for Calibration Based on Analytical and Mathematical Approaches
4.4.1. Bayesian Calibration
4.4.3. Optimization-Based Methods
5. Model Uncertainties
|Scenario uncertainty||Outdoor weather conditions|
|Building usage/occupancy schedule|
|Building physical/operational uncertainty||Building envelope properties|
|Operation and control settings|
|Model inadequacy||Modeling assumptions|
|Simplification in the model algorithm|
|Ignored phenomena in the algorithm|
|Observation error||Metered data accuracy|
5.1. Screening-Based Method
5.1.1. Sensitivity Index
5.1.2. Differential Sensitivity Analysis
- OP is the output data value;
- IP is the input data value; and
- the subscript bc indicates the values referring to the baseline model.
5.1.3. Elementary Effects
- Y is the system output evaluated before and after the variation of the ith parameter; and
- Δ is an incremental effect that is a multiple of 1/(p − 1).
5.2. Regression Analysis
5.3. Variance-Based Method
- first-order index, Si, which represents the effect of the input parameter Xi on output variation y;
- total order index, STi, that measures the effect of the parameter alone and the sensitivity of the interaction of the parameter with all other parameters, as described in Equation (16).
5.4. Monte Carlo Method
6. Calibrated Simulation Applications
- the calibration methodology adopted;
- the calibration level pursued;
- the model complexity;
- the simulation tool used; and
- the integration of SA/UA in the calibration process.
|Model type||Calibration level||Calibration Method||SA/UA||Monitoring period||Simulation tool or Standard|
|Palomo del Barrio, E.; Guyon, G.||Application of parameters space analysis tools for empirical model validation||2004||Energy and Buildings, 36, 23-33||||-||whole building model||-||-||Optmi-zation||SA||-||CLIM2000|
|Liu, S.; Henze, G.P.||Calibration of building models for supervisory control of commercial building||2005||9th International Building Simulation Association (IBPSA) Conference 2005||||Detailed||whole building model||-||Automated||Optmi-zation||-||-||EnergyPlus, GenOpt|
|Pan, Y.; Huang, Z.; Wu, G.||Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai||2007||Energy and Buildings, 39, 651-657||||Detailed||whole building model||Level 3||Manual||Iterative||-||-||DOE-2|
|Reddy, T.A.; Maor, I.; Panjapornpon, C.||Calibrating Detailed Building Energy Simulation Programs with Measured Data–Part II: Application to Three Case Study Office Buildings (RP-1051)||2007||HVAC and Research, 13, 221-241||||Detailed||whole building model||Level 4||Mathema-tical||-||Montecarlo||N.A.||DOE-2|
|Hassan, M.A.; Shebl, S.S.; Ibrahim, E.A.; Aglan, H.A.||Modeling and validation of the thermal performance of an affordable, energy efficient, healthy dwelling unit||2011||Journal of Building Simulation 4, 255-262||||Detailed||whole building model||Level 4-5||Manual||Iterative||-||Short-term||Visual DOE-4|
|Liu, G.; Liu, M.||A rapid calibration procedure and case study for simplified simulation models of commonly used HVAC systems||2011||Building and Environment 46, 409-420||||-||whole building model||Level 4||Graphical||Calibration Signature||NA||Short-term||-|
|Raftery, P.; Keane, M.; Costa, A.||Calibrating whole building energy models: Detailed case study using hourly measured data||2011||Energy and Buildings 2011, 43, 3666-3679||||Detailed||whole building model||Level 4||Manual||Iterative||-||Long-term||EnergyPlus|
|Bertagnolio, S.; Randaxhe, F.; Lemort, V.||Evidence-based calibration of a building energy simulation model: Application to an office building in Belgium||2012||12th International Conference for Enhanced Building Operations, Manchester, UK||||Normative (quasi-steady)||whole building model||Level 1 to 4||-||evidence-based||Morris Method||Short-term||ISO 13790|
|Heo, Y.; Choudhary, R.; Augenbroe, G.A.||Calibration of building energy models for retrofit analysis under uncertainty||2012||Energy and Buildings 47, 550-560||||Normative (quasi-steady)||whole building model||-||Mathema-tical||Bayesian||Morris Method||-||ISO 13790|
|Fontanella,G.; Basciotti, D.; Dubisch, F.; Judex, F.; Preisler, A.; Hettfleisch, C.; Vukovic, V.; Selke, T.||Calibration and validation of a solar thermal system model in Modelica||2012||Journal of Building Simulation 5, 293-300||||Detailed||Solar System||Level 4||-||Optmiza-tion||-||Short-term||Modelica (Dymola), GenOpt|
|Maile, T.; Bazjanac, T.; Fischer, M.||A method to compare simulated and measured data to assess building energy performance||2012||Building and Environment 56, 241-251||||Detailed||whole building model||N.A.||Manual||Iterative||-||Long-term||Not specified|
|Parker, J.; Cropper, P.; Shao, L.||A calibrated whole building simulation approach to assessing retrofit options for Birmingham airport||2012||IBPSA-England, 1st Building Simulation and Optimization Conference, Loughborough, UK||||Detailed||whole building model||Level 2||Manual (Raftery et al.)||Iterative||-||Long-term||IES|
|Kim, Y.; Yoon, S.; Park, C.||Stochastic comparison between simplified energy calculation and dynamic simulation||2013||Energy and Buildings 64, 332-342||||Simplified (A), detailed (B)||whole building model||-||Matema-tical||Bayesian||SA-Morris Method||-||ISO 13790 (A), EnergyPlus (B)|
|Manfren, M.; Aste, N.; Moshksar, R.||Calibration and uncertainty analysis for computer models–A meta-model based approach for integrated building energy simulation||2013||Applied Energy 103, 627-641||||Simplified and detailed||whole building model||Level 4||Mathema-tical||Bayesian, Meta-modelling||with Bayesian calibration||Short-term||-|
|O’Neill, Z.; Eisenhower, B.||Leveraging the analysis of parametric uncertainty for building energy model calibration||2013||Journal of Building Simulation 5, 365-377||||meta-model||whole building model||Levels 4-5||Automated||Optmi-zation||quasi-Montecarlo approach||Long-term||EnergyPlus, Design-Builder|
|Taheri, M.; Tahmasebi, F.; Mahdavi, A.||A case study of optimization-aided thermal building performance simulation calibration||2013||13th Conference of IBPSA Chambéry, France||||Dynamic||whole building model||Level 4||Automated||Optmi-zation||-||Short-term||EnergyPlus, GenOpt|
|Mihai, A.; Zmeureanu, R.||Calibration of an energy model of a new research center building||2014||13th Conference of IBPSA Chambéry, France||||Dynamic||whole building model||Level 4||Manual||evidence-based||-||Short-term||eQuest|
|Mustafaraj, G.; Marini, D.; Costa, A.; Keane, M.||Model calibration for building energy efficiency simulation||2014||Applied Energy 130, 72-85||||Dynamic||whole building model||Level 3-4||Manual||Iterative (based on Bertagnolio and Raftery methods)||SA||Short-term||Design-Builder, EnergyPlus|
|Penna, P.; Gasparella, A.; Cappelletti, F.; Tahmasebi, F.; Mahdavi A.||Optimization-based calibration of a school building based on short-term monitoring data||2014||10th European Conference on Product and Process Modeling||||Detailed||whole building model||Level 3-4||Automated||Optmiza-tion||-||Short-term||TRNSYS, GenOPt|
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