How Do Different Methods for Generating Future Weather Data Affect Building Performance Simulations? A Comparative Analysis of Southern Europe
Abstract
:1. Introduction
2. Materials and Methods
2.1. Future Weather Data for Building Simulation
- CCWorldWeatherGen. Version 1.9 of this free online tool was used in this work, which employs the HadCM3 climate model, developed by the Sustainable Energy Research Group of the University of Southampton [27]. It uses the morphing methodology to produce EPW future weather data files from original TMY files. In this research, two data sets for the time slices 2041–2070 (2050 scenario) and 2071–2100 (2080 scenario) were generated, morphed from the International Weather for Energy Calculation (IWEC) TMY file of Seville [28].
- Meteonorm. Two versions of this software were used: v7, which employs the HadCM3 climate model, and v8, whose update involves the use of the RCP8.5 scenario. Both versions of this software were developed by Intersolar Europe [29]. This software is classified within the stochastic weather generators (another method for statistical downscaling that implements computer algorithms). These generators rely on a statistical analysis of recorded climate data to produce long synthetic weather series. The software integrates its own climate database, from which hourly weather data from Seville from 2010 to 2020 were applied to this research. For the climate change evaluation, the typical meteorological years of 2050 (time slices 2046–2065) and 2080 (2080–2099) were generated for the city of Seville.
2.2. Building Simulation Models
- Test Cell. The first model simulates the behavior of a test cell (Figure 1). This one was analyzed given that its geometry reproduces a typical bedroom space of the social residential stock in southern Spain while being a simplified but controlled and highly monitored environment. The cell consists of a brick facade facing south (U-value = 1.43 W/m2K) with a double-glazed window (U-value = 3.3 W/m2K). The rest of the cell envelope consists of highly insulated walls, a roof, and a floor, reproducing near adiabatic behavior (U-value = 0.05 W/m2K).
- Multi-family building. The second model used in this work represents a linear multi-family housing building built in the 1960s (Figure 2). Its morphological and constructive typology represents more than 40% of southern Spain’s social housing existing stock, being one of the most predominant building typologies of this region and, thus, a crucial real building archetype. Its brick facade has a U-value of 1.58 W/m2K, with simple-glazed windows (U-value = 5.7 W/m2K), and the roof has a U-value of 1.82 W/m2K.
2.3. Adaptive Thermal Comfort and Energy Demand Assessment
- For the winter period (from December to February): EN 16798-1:2019 [35], which defines the optimum comfort temperature (Tc) according to Equation (1); in other words, it is directly dependent on outdoor temperatures. The percentage of discomfort hours is determined by considering the simulated indoor temperatures in the models that exceed the established adaptive comfort band. For the definition of the aforementioned comfort band, an acceptability range according to building category III (for a moderate level of expectation) was applied, which means a predicted percentage dissatisfied (PPD) under 15% and sets a temperature interval of +4 °C (upper comfort band limit) and −5 °C (lower comfort band limit). Thus, hourly simulation results must be obtained.
- For the summer, spring, and autumn periods (from March to November): Equation (3), defined by Barbadilla-Martín [36] for the specific case of “Mixed Mode” buildings (naturally ventilated through windows and with cooling systems for occasional use) in southern Spain. The methodology for the calculation is similar to the previous one, but in this case, the acceptability range considered corresponds to a PPD under 20% and a temperature interval of ±3.5 °C, which defines the adaptive comfort band. This leads to an updated equation for the calculation of the optimum comfort temperature (Tc).
3. Results and Discussion
3.1. Weather Data Analysis
3.2. Thermal Comfort and Energy Demand Evaluation
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Pattern | Schedule | |
---|---|---|
23.00–7.00 h | 7.00–23.00 h | |
Heating | 17 °C | 20 °C |
Cooling | 27 °C | 25 °C |
Tool | CGM | Base File 1 | Future Scenarios 1 |
---|---|---|---|
CCWorldWeatherGen | HadCM3 | IWEC TMY (1982–2006) | 2050 (2041–2070) 2080 (2071–2100) |
Meteonorm v7 | HadCM3 | Meteonorm database (2010–2020) | 2050 (2046–2065) 2080 (2080–2099) |
Meteonorm v8 | RCP8.5 | Meteonorm database (2010–2020) | 2050 (2046–2065) 2080 (2080–2099) |
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Escandón, R.; Calama-González, C.M.; Alonso, A.; Suárez, R.; León-Rodríguez, Á.L. How Do Different Methods for Generating Future Weather Data Affect Building Performance Simulations? A Comparative Analysis of Southern Europe. Buildings 2023, 13, 2385. https://doi.org/10.3390/buildings13092385
Escandón R, Calama-González CM, Alonso A, Suárez R, León-Rodríguez ÁL. How Do Different Methods for Generating Future Weather Data Affect Building Performance Simulations? A Comparative Analysis of Southern Europe. Buildings. 2023; 13(9):2385. https://doi.org/10.3390/buildings13092385
Chicago/Turabian StyleEscandón, Rocío, Carmen María Calama-González, Alicia Alonso, Rafael Suárez, and Ángel Luis León-Rodríguez. 2023. "How Do Different Methods for Generating Future Weather Data Affect Building Performance Simulations? A Comparative Analysis of Southern Europe" Buildings 13, no. 9: 2385. https://doi.org/10.3390/buildings13092385
APA StyleEscandón, R., Calama-González, C. M., Alonso, A., Suárez, R., & León-Rodríguez, Á. L. (2023). How Do Different Methods for Generating Future Weather Data Affect Building Performance Simulations? A Comparative Analysis of Southern Europe. Buildings, 13(9), 2385. https://doi.org/10.3390/buildings13092385