Analysis of uncertainty indices used for building envelope calibration

作者：Ramos Ruiz German; Fernandez Bandera Carlos

来源：Applied Energy, 2017, 185: 82-94.

DOI：10.1016/j.apenergy.2016.10.054

• Multi-zone calibration
• Energy simulation
• Uncertainty analysis
• Multi-objective optimisation
• Genetic algorithm (NSGA-II)

Nowadays there is a growing concern about climate change and the global warming effect produced by the concentration of greenhouse gases (GHG). At the Paris climate conference (COP21), 195 countries adopted a global climate agreement, limiting global warming to well below 2 degrees C. Buildings are large producers of GHG and therefore international standards such as ISO 50001 focus on improving their energy performance, including energy efficiency, use and consumption. To achieve this goal it is important to have a detailed knowledge of the thermal behaviour of buildings. The International Performance Measurement and Verification Protocol (IPMVP), proposes a calibrated simulation model (Option D) to gather this knowledge and to determine the savings associated with Energy Conservation Measures (ECMs).
This paper improves the calibration methodology proposed by Ramos et al. (2016) [1], solving the limitations regarding the number of thermal zones and the use of free-floating time periods. Through a real case-study that guides the process, the paper explains how to achieve a calibrated Building Energy Simulation (BES) model using an optimisation process based on a meta-heuristic strategy (genetic algorithm - NSGA-II). Different uncertainty indices such as: CV(RMSE) and Goodness of Fit (GOF) are used as objective function to obtain the calibrated model. These indices, frequently used to measure the accuracy of models, are combined to provide a double possibility to find the best solution, as they are an objective function and a model accuracy measure.

• 出版日期2017-1-1