Accuracy Assessment of Typical Meteorological Year Data for a Photovoltaic System using a Bootstrap Method
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (25)
- De Miguel, A., "Test Reference Year Generation from Meteorological and Simulated Solar Radiation Data", Solar Energy, 78(6), pp.695~703 (2005)
- 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)
- 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)
- Generation of Typical Meteorological Years for 26 SOLMET Stations, "Hall, I.", Sandia National Lab, ~, pp.Rapport technique SAND78-1601 (1978)
- 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)
- Cui, Y., "Comparison of Typical Year and Multiyear Building Simulations Using a 55-year Actual Weather Dataset from China", Applied Energy, 195, pp.890~904 (2017)
- Bre, F., "Generation of Typical Meteorological Years for the Argentine Littoral Region", Energy and Buildings, 129, pp.432~444 (2016)
- Ho, C. K., "Methods for Probabilistic Modeling of Concentrating Solar Power Plants", Solar Energy, 85(4), pp.669~675 (2011)
- Nelken, K., "TMY Versus Multi-year Time Series of Meteorological Conditions for the Characterization of Central Poland’s Suitability for Photovoltaics", Meteorologische Zeitschrift, 26(1), pp.21~31 (2016)
- Khosravi, A., "Prediction Intervals for Short-term Wind Farm Power Generation Forecasts", IEEE Transactions on Sustainable Energy, 4(3), pp.602~610 (2013)
- Chu, Y., "Short-term Probabilistic Forecasts for Direct Normal Irradiance", Renewable Energy, 101, pp.526~536 (2017)
- Hu, Y. M., "Uncertainty Analysis of SPI Calculation and Drought Assessment Based on the Application of Bootstrap", International Journal of Climatology, 35(8), pp.1847~1857 (2015)
- Ohunakin, O. S., "Generation of a Typical Meteorological Year for North-east, Nigeria", Applied Energy, 112, pp.152~159 (2013)
- Realpe, A., "Benchmarking of Five Typical Meteorological Year Datasets Dedicated to Concentrated-PV Systems", Energy Procedia, 97, pp.108~115 (2016)
- Nelken, K., "TMY Versus Multi-year Time Series of Meteorological Conditions for the Characterization of Central Poland’s Suitability for Photovoltaics", Meteorologische Zeitschrift, 26(1), pp.21~31 (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)
- 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)
- Carigiet, F, "Verification of Measured PV Energy Yield Versus Forecast and Loss Analysis", 28th European PV Solar Energy Conference (2013)
- Rostron, P. D., "Confidence Intervals for Robust Estimates of Measurement Uncertainty", Accreditation and Quality Assurance, pp.1~13 (2020)
- Tsamardinos, I., "Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-validation", Machine Learning, 107(12), pp.1895~1922 (2018)
- Ahn, H., "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, 255, pp.113753~ (2019)
- Khosravi, A., "Prediction Intervals for Short-term Wind Farm Power Generation Forecasts", IEEE Transactions on Sustainable Energy, 4(3), pp.602~610 (2013)
- Jiang, Y., "Generation of Typical Meteorological Year for Different Climates of China", Energy, 35(5), pp.1946~1953 (2010)
- Chan, A. L. S., "Generation of Typical Meteorological Years Using Genetic Algorithm for Different Energy Systems", Renewable Energy, 90, pp.1~13
- Pattarapanitchai, S., "Generation of Typical Meteorological Year Datasets for 20 Stations in Thailand", 2014 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), pp.1~6 (2014), IEEE