Impact of Weather Files Influenced on Building Energy Consumption Assessment
Article Sidebar
Main Article Content
Abstract
In recent years, Thailand has experienced significant climate variations, which have influenced the building energy consumption using computer simulation programs. These assessments rely on weather data files to analyze energy consumption. However, discrepancies between weather data files and actual climatic conditions may impact the accuracy of such evaluations. This study examines four weather data files representing Bangkok’s climate 1) EnergyPlus Weather (EPW) 2) Climate One Building (COB) 3) Photovoltaic Geographical Information System (PVGIS) 4) Integrated Environmental Solutions (IES) For comparison, actual climatic data in this study are derived from the weather monitoring station at the Faculty of Architecture, Chulalongkorn University (CU).
To evaluate how different climate data sets affect building energy performance simulation, temperature, relative humidity, direct and diffuse solar radiation, and Cooling Degree Days (CDD) were compared. The Chamchuri 5 building was selected as the case study for simulating cooling energy consumption. The analysis revealed that climate data from online sources differed significantly from the CU weather data in all parameters, except for the temperature data from COB, which showed no significant difference. However, when all climate data sets were used to simulate cooling energy performance using building energy software and compared to CU data, only PVGIS exhibited a statistically significant difference. Furthermore, insulation of the building envelope was identified as a critical factor influencing energy consumption due to the impact of climatic variations.
Article Details
References
ณัฏฐา ตระกูลไทย. (2558). ผลกระทบจากภาวะอากาศเปลี่ยนแปลงต่อการใช้พลังงานอาคารในเขตร้อนชื้น [วิทยานิพนธ์ ปริญญามหาบัณฑิต, จุฬาลงกรณ์มหาวิทยาลัย]. CUIR. http://cuir.car.chula.ac.th/handle/123456789/51131
ปกป้อง ปัตทวีคงคา. (2555). อิทธิพลของภูมิอากาศในภูมิภาคต่าง ๆ ของประเทศไทยต่อการใช้พลังงานในอาคาร [วิทยานิพนธ์ปริญญามหาบัณฑิต, จุฬาลงกรณ์มหาวิทยาลัย]. CUIR. http://cuir.car.chula.ac.th/handle/123456789/42524
ปรีชา อาการศ และกูสกานา กูบาฮ. (2562). การศึกษาความสัมพันธ์ระหว่างอุณหภูมิภายนอกและการใช้พลังงานของเครื่องปรับอากาศของอาคารอเนกประสงค์มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าธนบุรี, วารสารวิชาการ ปขมท., 80(2), 80–89.
จุฬาลงกรณ์มหาวิทยาลัย. สำนักบริหารระบบกายภาพ. (2563). แบบก่อสร้างอาคารจามจุรี 5 จุฬาลงกรณ์มหาวิทยาลัย CEN062. สำนัก.
อภิญญา เวชกามา. (2565). ผลกระทบจากภาวะอากาศเปลี่ยนแปลงกับการออกแบบพลังงานหมุนเวียน เพื่อไปสู่อาคารพักอาศัย ปล่อยคาร์บอนสุทธิเป็นศูนย์ [วิทยานิพนธ์ปริญญามหาบัณฑิต, จุฬาลงกรณ์มหาวิทยาลัย]. Chula DigiVerse. https://digiverse.chula.ac.th/Info/item/dc:51879
ASHRAE. (2002). ASHRAE Guideline: Measurement of energy and demand savings. https://www.eeperformance.org/uploads/8/6/5/0/8650231/ashrae_guideline_14-2002_measurement_of_energy_and_demand_saving.pdf
ASHRAE. (2013). ASHRAE handbook fundamentals (SI ed.). American Society of Heating, Refrigerating and Air-Conditioning Engineers. https://www.ashrae.org
ASHRAE. (2021). ASHRAE handbook-Fundamentals. https://www.ashrae.org/technical-resources/ashrae-handbook/ashrae-handbook-online
Crawley, D., & Lawrie, L. (2014). Climate one building. https://climate.onebuilding.org/
Crawley, D. B., Hand, J. W., Kummert, M., Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building and Environment, 43(4), 661–673.
EnergyPlus. (n.d.). EnergyPlus. https://energyplus.net/
European Center for Medium-Range Weather Forecasts. (2005). Photovoltaic Geographical Information System (PVGIS). https://joint-research-centre.ec.europa.eu/
Fatek Group. (n.d.). SP-PU-01 REV.00: Specification of PU panel. The company.
Guardian Glass. (2022). Guardian GlassTime: Technical manual. Guardian Europe. file:///C:/Users/maplab/Downloads/GlassTimeHandbook_EN_0822.pdf
Gupta, V., & Deb, C. (2023, October). Envelope design for low-energy buildings in the tropics: A review. Renewable and Sustainable Energy Reviews, 186, 113650.
Integrated Environment Solutions (IES). (1995). Athenium analytics: Weather data for any global location. https://www.iesve.com/support/weatherfiles/athenium-analytics
Internal Panel in Climate Change. (2023). Climate change 2023 synthesis report. https://www.ipcc.ch/report/ar6/syr/
International Business Machines [IBM]. (2021). IBM SPSS statistics 29 core system user's guide. https://www.ibm.com/docs/en/SSLVMB_29.0.0/pdf/IBM_SPSS_Statistics_Core_System_User_Guide.pdf
Liu, Y., Stouffs, R., Tablada, A., Wong, N. H., & Zhang, J. (2016). Comparing micro-scale weather data to building energy consumption in Singapore. Energy and Building, 152(1), 776–791.
Radhi, H. (2008). A comparison of the accuracy of building energy analysis in Bahrain using data from different weather periods. Renewable Energy, 34(3), 869–875.
Sakurai, Y. (2023). Analysis of design weather data used in the Asian region [Unpublished master’s thesis]. Tokyo Metropolitan University.
Santamouris, M. (2016, September 15). Cooling the buildings – past, present and future. Energy and Buildings, 128, 617–638.
U.S. Department of Energy. (2022, March 29). EnergyPlus™ version 22.1.0 documentation: Auxiliary programs. The Department.
Wang, W., Li, S., Guo, S., Ma, M., Feng, S., Bao, L. (2021, November). Benchmarking urban local weather with long-term monitoring compared with weather datasets from climate station and EnergyPlus weather (EPW) data. Energy Reports, 7, 6501–6514.
Wilcox, S., & Marion, W. (2008, May). User’s manual forTMY3 data sets: Technical report NREL/TP-581-43156. National Renewable Energy Laboratory.
World Meteorological Organization. (2023). The global climate 2011–2020: A decade of accelerating climate change. https://www.un-ilibrary.org/content/books/9789263113382c004
Yassaghi, H., Mostafavi, N., Hoque, S. (2019, September 15). Evaluation of current and future hourly weather data intended for building designs: A Philadelphia case study. Energy and Buildings, 199, 491–511.
