Multi-Year and Typical Year Data for Solar Energy System in South Korea
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Typical meteorological year (TMY) data has significant importance for solar resource assessment, as well as for building performance analysis. The necessity of high-accuracy TMY data has been well known for many years for the financial viability of solar long-term planing project as it represents long-term weather features. However, the TMY does not mean the use of real-time data; it is therefore important to determine the accuracy of the TMY dataset. In this study, the solar energy performance of TMY was evaluated quantitatively by comparing it to multi-year average weather data (2008-2017) based on the statistical analysis method. The PV power output were produced by using 100kW photovoltaic system based on PVsyst software at six sites. The results showed that the difference between the TMY-PV power output and long-term PV power output (measured with percentage error) are smaller than 9%, which means there is close-fit agreement between TMY and long-term averages PV power output. These findings suggests that TMY can provide reliable estimation of PV power in the feasibility study of PV project.
Abstract
Typical meteorological year (TMY) data has significant importance for solar resource assessment, as well as for building performance analysis. The necessity of high-accuracy TMY data has been well known for many years for the financial viability of solar long-term planing project as it represents long-term weather features. However, the TMY does not mean the use of real-time data; it is therefore important to determine the accuracy of the TMY dataset. In this study, the solar energy performance of TMY was evaluated quantitatively by comparing it to multi-year average weather data (2008-2017) based on the statistical analysis method. The PV power output were produced by using 100kW photovoltaic system based on PVsyst software at six sites. The results showed that the difference between the TMY-PV power output and long-term PV power output (measured with percentage error) are smaller than 9%, which means there is close-fit agreement between TMY and long-term averages PV power output. These findings suggests that TMY can provide reliable estimation of PV power in the feasibility study of PV project.
참고문헌 (32)
- Soares, N., "A Review on Current Advances in the Energy and Environmental Performance of Buildings Towards a More Sustainable Built Environment", Renewable and Sustainable Energy Reviews, 77, pp.845~860 (2017)
- Despotovic, M., "Evaluation of Empirical Models for Predicting Monthly Mean Horizontal Diffuse Solar Radiation", Renewable and Sustainable Energy Reviews, 56, pp.246~260 (2016)
- Kolokotsa, D. E. K. D., "A Roadmap Towards Intelligent Net Zero-and Positive-energy Buildings", Solar Energy, 85(12), pp.3067~3084 (2011)
- Mohanty, S., "Prediction and Application of Solar Radiation with Soft Computing over Traditional and Conventional Approach—A Comprehensive Review", Renewable and Sustainable Energy Reviews, 56, pp.778~796 (2016)
- Crawley, D. B., "Does It Matter Which Weather Data You Use in Energy Simulations", User News, 18(1), pp.2~12 (1997)
- Janjai, S., "Comparison of Methods for Generating Typical Meteoro-logical Year Using Meteorological Data from a Tropical Environment", Applied Energy, 86, pp.528~537 (2009)
- ISO, "ISO 15927-4:2005, Hygrothermal Performance of Buildings e Calculations and Presentation of Climatic Data e Part 4: Hourly Data for Assessing the Annual Energy Use for Heating and Cooling", European Committee for Standardization, ~ (2005)
- Moazami, A., "Impacts of Future Weather Data Typology on Building Energy Performance–Investigating Long-term Patterns of Climate Change and Extreme Weather Conditions", Applied Energy, 238, pp.696~720 (2019)
- Hee, W. J., "The Role of Window Glazing on Daylighting and Energy Saving in Buildings", Renewable and Sustainable Energy Reviews, 42, pp.323~343 (2015)
- Generation of Typical Meteorological Years for 26 SOLMET Stations, "Hall, I.", Sandia National Lab, ~, pp.Rapport technique SAND78-1601 (1978)
- Selection methods for production of Test Reference Years, "Lund, H.", Tech. Univ. Denmark, Dept. of Buildings and Energy, ~, pp.Report no EUR 7306 EN (1985)
- Chan, A. L., "Generation of a Typical Meteorological Year for Hong Kong", Energy Conversion and management, 47(1), pp.87~96 (2006)
- Lee, K., "Generation of Typical Weather Data Using the ISO Test Reference Year(TRY)Method for Major Cities of South Korea", Building and Environment, 45(4), pp.956~963 (2010)
- Kim, Y., "Study on Extension of Standard Meteorological Data for Cities in South Korea Using ISO 15927-4", Atmosphere, 8(11), pp.220~ (2017)
- 지준범, "한반도 태양에너지 연구를 위한 일사량 자료의 TMY 구축", 신재생에너지, 8(2), pp.14~23 (2012), 한국신·재생에너지학회[DOI]
- Ohunakin, O. S., "Generation of a Typical Meteorological Year for North-East, Nigeria", Applied Energy, 112, pp.152~159 (2013)
- Argiriou, A., "Comparison of Methodologies for TMY Generation Using 20 Years Data for Athens", Greece, Sol Energy, 66(1), pp.33~45 (1999)
- Pattarapanitchai, S., "Generation of Typical Meteorological Year Data Sets for 20 Stations in Thailand", pp.1~6 (2014)
- Yilmaz, S., "The Generation of Typical Meteorological Year and Climatic Database of Turkey for the Energy Analysis of Buildings", Journal of Environmental Science and Engineering A, 6, pp.370~376 (2017)
- Skeiker, K., "Comparison of Methodologies for TMY Generation Using 10 Years Data for Damascus, Syria", Energy Conversion and Management, 48(7), pp.2090~2102 (2007)
- Rahman, I. A., "Selection of Typical Weather Data (Test Reference Years) for Subang, Malaysia", Building and Environment, 42(10), pp.3636~3641 (2007)
- De Miguel, A., "Test Reference Year Generation from Meteorological and Simulated Solar Radiation Data", Solar Energy, 78(6), pp.695~703 (2005)
- Zhang, Q., "Chinese Typical Year Weather Data for Architectural Use", ~ (2004)
- Pusat, S., "Generation of Typical Meteorological Year for Different Climates of Turkey", Renewable Energy, 75, pp.144~151 (2015)
- Moreno-Tejera, S., "Solar Resource Assessment in Seville, Spain, Statistical Characterisation of Solar Radiation at Different Time Resolutions", Sol. Energy, 132, pp.430~441 (2016)[DOI]
- Kim, C. K., "Intercomparison of Satellite-Derived Solar Irradiance from the GEO-KOMSAT-2A and HIMAWARI-8", 9 Satellites by the Evaluation with Ground Observations, 2020, pp.12(13), 2149~
- Carigiet, F., "October. Verification of measured PV Energy Yield Versus Forecast and Loss Analysis", 28th European PV Solar Energy Conference (2013)
- Kariuki, B. W., "Interannual and Spatial Variability of Solar Radiation Energy Potential in Kenya Using Meteosat Satellite", Renewable energy, 116, pp.88~96 (2018)
- Copper, J. K., "Interannual Variability of the Solar Resource Across Australia", Proceedings of the Asia Pacific Solar Research Conference (2017)
- Realpe, A., "Benchmarking of Five Typical Meteorological Year Dataset Dedicated to Concentrated-PV Systems", Energy Procedia, 97, pp.108~115 (2016)
- Abreu, E. F., "Solar Resource Assessment through Long-term Statistical Analysis and Typical Data Generation with Different Time Resolutions Using GHI Measurements", Renewable Energy, 127, pp.398~411 (2018)
- Jiang, Y., "Generation of Typical Meteorological Year for Different Climates of China", Energy, 35(5), pp.1946~1953 (2010)