Accuracy Assessment of Typical Meteorological Year Data for a Photovoltaic System using a Bootstrap Method

Laetitia Uwineza; Hyun-Goo Kim; Chang Ki Kim; Boyoung Kim; Jin-Young Kim

doi:10.7836/kses.2021.41.4.115

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2021 Vol.41, Issue 4 Preview Page Next Page

Research Article

Accuracy Assessment of Typical Meteorological Year Data for a Photovoltaic System using a Bootstrap Method

30 August 2021. pp. 115-129

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Abstract

The typical meteorological year (TMY) is a key element in the long-term performance predictions of photovoltaic systems. However, there is a loss of some hourly measured weather data during the formation of the TMY dataset owing to a lack of observations. Therefore, various statistical techniques or satellite observations can be utilized to address this limitation, and these interpolated data may represent some degree of uncertainty that can influence the accuracy of the TMY data. This may in turn affect the long-term planning reliability of the photovoltaic systems. Therefore, a bootstrap method was used in this study to evaluate the accuracy of TMY datasets in the prediction of long-term photovoltaic yields. The electricity production uncertainties obtained through a simulation conducted using this TMY were calculated with a confidence level of 95% for each resulting value as validated based on the long-term average electricity yield. The results show that the bootstrap method provides valid and more useful information than the deterministic approach. Therefore, it is the best method for quantitatively analyzing uncertainty in TMY datasets. The results described herein can serve as a guide for risk management strategies and other business decisions related to solar energy projects. In addition, the results can help planners achieve greater confidence when applying TMY data to feasibility studies.

Keywords

Photovoltaic system

Typical meteorological year

Bootstrap method

References

De Miguel, A. and Bilbao, J., Test Reference Year Generation from Meteorological and Simulated Solar Radiation Data, Solar Energy, Vol. 78, No. 6, pp. 695-703, 2005. 10.1016/j.solener.2004.09.015

Crawley, D. B., Which Weather Data Should You Use for Energy Simulations of Commercial Buildings?, Transactions-American Society of Heating Refrigerating and Air Conditioning Engineers, 104, pp. 498-515, 1998.

Lund, H. and Eidorff, S., Selection Methods for Production of Test Reference Years, Report no EUR 7306 EN. - Tech. Univ. Denmark, Dept. of Buildings and Energy, 1985.

Hall, I., Prairie, R., Anderson, H., and Boes, E., Generation of Typical Meteorological Years for 26 SOLMET Stations, Rapport technique SAND78-1601, - Sandia National Lab., Albuquerque, 1978.

Moazami, A., Nik, V. M., Carlucci, S., and Geving, S., Impacts of Future Weather Data Typology on Building Energy Performance-Investigating Long-term Patterns of Climate Change and Extreme Weather Conditions, Applied Energy, Vol. 238, pp. 696-720, 2019. 10.1016/j.apenergy.2019.01.085

Cui, Y., Yan, D., Hong, T., Xiao, C., Luo, X., and Zhang, Q., Comparison of Typical Year and Multiyear Building Simulations Using a 55-year Actual Weather Dataset from China, Applied Energy, Vol. 195, pp. 890-904, 2017. 10.1016/j.apenergy.2017.03.113

Bre, F. and Fachinotti, V. D., Generation of Typical Meteorological Years for the Argentine Littoral Region, Energy and Buildings, Vol. 129, pp. 432-444, 2016. 10.1016/j.enbuild.2016.08.006

Ho, C. K., Khalsa, S. S., and Kolb, G. J., Methods for Probabilistic Modeling of Concentrating Solar Power Plants, Solar Energy, Vol. 85, No. 4, pp. 669-675, 2011. 10.1016/j.solener.2010.05.004

Nelken, K. and Zmudzka, E., TMY Versus Multi-year Time Series of Meteorological Conditions for the Characterization of Central Poland’s Suitability for Photovoltaics, Meteorologische Zeitschrift, Vol. 26, No. 1, pp. 21-31, 2016. 10.1127/metz/2016/0755

Khosravi, A., Nahavandi, S., and Creighton, D., Prediction Intervals for Short-term Wind Farm Power Generation Forecasts, IEEE Transactions on Sustainable Energy, Vol. 4, No. 3, pp. 602-610, 2013. 10.1109/TSTE.2012.2232944

Chu, Y. and Coimbra, C.F., Short-term Probabilistic Forecasts for Direct Normal Irradiance, Renewable Energy, 101, pp. 526-536, 2017. 10.1016/j.renene.2016.09.012

Hu, Y .M., Liang, Z. M., Liu, Y. W., Wang, J., Yao, L., and Ning, Y., Uncertainty Analysis of SPI Calculation and Drought Assessment Based on the Application of Bootstrap, International Journal of Climatology, Vol. 35, No. 8, pp. 1847-1857, 2015. 10.1002/joc.4091

Ohunakin, O. S., Adaramola, M. S., Oyewola, O. M. and Fagbenle, R. O., Generation of a Typical Meteorological Year for North-east, Nigeria, Applied Energy, Vol. 112, pp. 152-159, 2013. 10.1016/j.apenergy.2013.05.072

Realpe, A., Vernay, C., Pitaval, S., Lenoir, C., and Blanc, P., Benchmarking of Five Typical Meteorological Year Datasets Dedicated to Concentrated-PV Systems, Energy Procedia, Vol. 97, pp.108-115, 2016. 10.1016/j.egypro.2016.10.031

Abreu, E. F., Canhoto, P., Prior, V., and Melicio, R., Solar Resource Assessment through Long-term Statistical Analysis and Typical Data Generation with Different Time Resolutions Using GHI Measurements, Renewable Energy, Vol. 127, pp. 398-411, 2018. 10.1016/j.renene.2018.04.068

Moreno-Tejera, S., Silva-Perez, M. A., Lillo-Bravo, I., and Ramírez-Santigosa, L., Solar Resource Assessment in Seville, Spain, Statistical Characterisation of Solar Radiation at Different Time Resolutions, Sol. Energy, Vol. 132, 430e441, 2016. 10.1016/j.solener.2016.03.032

Carigiet, F., Baumgartner, F., Sutterlueti, J., Allet, N., Pezzotti, M., and Haller, J., Verification of Measured PV Energy Yield Versus Forecast and Loss Analysis, In 28th European PV Solar Energy Conference, October 2013 .

Rostron, P. D., Fearn, T., and Ramsey, M. H., Confidence Intervals for Robust Estimates of Measurement Uncertainty, Accreditation and Quality Assurance, pp. 1-13, 2020. 10.1007/s00769-021-01457-9

Tsamardinos, I., Greasidou, E., and Borboudakis, G., Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-validation, Machine Learning, Vol. 107, No. 12, pp. 1895-1922, 2018. 10.1007/s10994-018-5714-430393425PMC6191021

Ahn, H., Rim, D., Pavlak, G. S., and Freihaut, J. D., Uncertainty Analysis of Energy and Economic Performances of Hybrid Solar Photovoltaic and Combined Cooling, Heating, and Power (CCHP+ PV) Systems Using a Monte-Carlo Method, Applied Energy, Vol. 255, 113753, 2019. 10.1016/j.apenergy.2019.113753

Jiang, Y., Generation of Typical Meteorological Year for Different Climates of China, Energy, Vol. 35, No. 5, pp. 1946-1953, 2010. 10.1016/j.energy.2010.01.009

Chan, A. L. S., Generation of Typical Meteorological Years Using Genetic Algorithm for Different Energy Systems, Renewable Energy, Vol. 90, pp. 1-13. 10.1016/j.renene.2015.12.052

Pattarapanitchai, S., Tohsing, K., Pankaew, P., and Janjai, S., Generation of Typical Meteorological Year Datasets for 20 Stations in Thailand, In 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE) (pp. 1-6), IEEE, March 2014.

Information

Publisher :Korean Solar Energy Society
Publisher(Ko) :한국태양에너지학회
Journal Title :Journal of the Korean Solar Energy Society
Journal Title(Ko) :한국태양에너지학회 논문집
Volume : 41
No :4
Pages :115-129
Received Date : 2021-04-07
Revised Date : 2021-06-01
Accepted Date : 2021-06-07
DOI :https://doi.org/10.7836/kses.2021.41.4.115

Journal of the Korean Solar Energy Society ISSN:1598-6411(Print) 2508-3562(Online) 한국태양에너지학회 논문집

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