Classification of Daily Irradiance Profiles and the Behaviour of Photovoltaic Plant Elements: The Effects of Cloud Enhancement
Source abstract
Abstract
In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Solar Radiation and Photovoltaics · Photovoltaic System Optimization Techniques · Energy and Environment Impacts
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronic and Computer Engineering, Campus de Rabanales, University of Córdoba, 14071 Córdoba, Spain
Funding
Spanish Ministry of Economy and Competitiveness
References (61)
Show source-provided references
1. Departement of Economic and Social Services (2021, March 15). Sustainable Development. United Nations: The Sustainable Goals. Available online: https://sdgs.un.org/es/goals.
2. IRENA (International Renewable Energy Agnecy) (2021, March 15). Double the Share of Renewables in the ‘Decade of Action’ to Achieve Energy Transition Objectives. Available online: https://www.irena.org/newsroom/pressreleases/2020/Jan/Double-the-Share-of-Renewables-in-the-Decade-of-Action-to-Achieve-Energy-Transition-Objectives.
3. European Commission (2021, March 15). A European Green Deal. Available online: https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en.10.4324/9781003246985-3 ↗
4. Unión Española Fotovoltaica (UNEF) (2020). The Photovoltaic Sector towards a New Era, Annual Report UNEF 2020, UNEF. Available online: https://unef.es/informacion-sectorial/informe-anual-unef/.
5. International Renewable Energy Agnecy (IRENA) (2020). The Post-COVID Recovery. An Agenda for Resilience, Development and Equality, IRENA. Available online: https://www.irena.org/publications/2020/Jun/Post-COVID-Recovery.
6. Electricity demand during pandemic times: The case of the COVID-19 in Spain10.1016/j.enpol.2020.111964 ↗
7. (2021, March 18). Solar Power Europe. Available online: https://www.solarpowereurope.org/.
8. International Energy Agency (2021, March 30). Photovoltaic Power System Programme. Available online: https://iea-pvps.org/.
9. A comprehensive assessment of the energy performance of the full range of electricity generation technologies deployed in the United Kingdom10.1016/j.enpol.2015.12.011 ↗
10. An Algorithm-based Approach to Map the Players’ Network for Photovoltaic Energy Businesses
11. Devetaković, M., Djordjević, D., Radojević, M., Krstić-Furundžić, A., Burduhos, B.-G., Martinopoulos, G., Neagoe, M., and Lobaccaro, G. (2020). Photovoltaics on Landmark Buildings with Distinctive Geometries. Appl. Sci., 10.10.3390/app10196696 ↗
12. Are rooftop photovoltaic systems a sustainable solution for Europe? A life cycle impact assessment and cost analysis10.1016/j.apenergy.2019.114035 ↗
13. Large scale integration of photovoltaics in cities10.1016/j.apenergy.2011.12.033 ↗
14. Santiago, I., Trillo-Montero, D., Luna-Rodríguez, J.J., Real-Calvo, R.J., and Moreno-Muñoz, A. (2015, January 17–19). Analysis of variations in PV production, focussing on storage and dispatchability decisions. Proceedings of the IEEE International Conference on Industrial Technology (ICIT), Seville, Spain.10.1109/ICIT.2015.7125523 ↗
15. Machine learning methods for solar radiation forecasting: A review10.1016/j.renene.2016.12.095 ↗
16. Review of photovoltaic power forecasting10.1016/j.solener.2016.06.069 ↗
17. Solar forecasting methods for renewable energy integration10.1016/j.pecs.2013.06.002 ↗
18. Photovoltaic and solar power forecasting for smart grid energy management10.17775/CSEEJPES.2015.00046 ↗
19. Solar photovoltaic generation forecasting methods: A review10.1016/j.enconman.2017.11.019 ↗
20. Ensemble methods for wind and solar power forecasting—A state-of-the-art review10.1016/j.rser.2015.04.081 ↗
21. Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators10.1109/TSG.2015.2397003 ↗
22. Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models10.1016/j.renene.2017.05.019 ↗
23. Du, J., Min, Q., Zhang, P., Guo, J., Yang, J., and Yin, B. (2018). Short-term solar irradiance forecasts using sky images and radiative transfer model. Energies, 11.10.3390/en11051107 ↗
24. Comparing various solar irradiance categorization methods—A critique on robustness10.1016/j.renene.2020.03.055 ↗
25. Photovoltaic power pattern clustering based on conventional and swarm clustering methods10.1016/j.solener.2015.11.010 ↗
26. Model-free computation of ultra-short-term prediction intervals of solar irradiance10.1016/j.solener.2015.11.017 ↗
27. Classification of days according to DNI profiles using clustering techniques10.1016/j.solener.2017.02.031 ↗
28. Classification des Eclairements Solaires à l’Aide de l’Analyse Fractale
29. Harrouni, S. (2008). Fractal classification of typical meteorological days from global solar irradiance: Application to five sites of different climates. Modeling Solar Radiation at the Earth’s Surface: Recent Advances, Springer.
30. Maafi, A., and Harrouni, S. (2003). Preliminary results of the fractal classification of daily solar irradiances. Sol. Energy.10.1016/S0038-092X(03)00192-0 ↗
31. Prediction of Solar Radiation Using Data Clustering and Time-Delay Neural Network
32. Cluster analysis for classification and forecasting of solar irradiance in Durban, South Africa
33. Investigating diffuse irradiance variation under different cloud conditions in Durban, using k-means clustering10.17159/2413-3051/2019/v30i3a6314 ↗
34. Chicco, G., Cocina, V., and Spertino, F. (October, January 28). Characterization of solar irradiance profiles for photovoltaic system studies through data rescaling in time and amplitude. Proceedings of the Universities Power Engineering Conference, Perth, Australia.
35. Badescu, V. (2008). Modeling Solar Radiation at the Earth’s Surface: Recent Advances, Springer.10.1007/978-3-540-77455-6 ↗
36. A Morphological Clustering Method for daily solar radiation curves10.1016/j.solener.2011.04.023 ↗
37. Clustering daily solar radiation from Reunion island using data analysis methods10.24084/repqj11.340 ↗
38. Santiago, I., Trillo Montero, D., Luna Rodríguez, J.J., Moreno Garcia, I.M., and Palacios Garcia, E.J. (2017). Graphical diagnosis of performances in photovoltaic systems: A case study in southern Spain. Energies, 10.10.3390/en10121964 ↗
39. Analytical performance monitoring of a 142.5kW p grid-connected rooftop BIPV system in Singapore10.1016/j.renene.2012.03.034 ↗
40. Analysis of the cloud enhancement phenomenon and its effects on photovoltaic generators based on cloud speed sensor measurements10.1063/5.0007550 ↗
41. Characteristics of the cloud enhancement phenomenon and PV power plants10.1016/j.solener.2019.11.090 ↗
42. Model-based clustering of high-dimensional data: A review10.1016/j.csda.2012.12.008 ↗
43. Overview and performance assessment of the clustering methods for electrical load pattern grouping10.1016/j.energy.2011.12.031 ↗
44. Müller, M. (2007). Information Retrieval for Music and Motion, Springer.10.1007/978-3-540-74048-3 ↗
45. Fuzzy clustering of time series data using dynamic time warping distance10.1016/j.engappai.2014.12.015 ↗
46. A global averaging method for dynamic time warping, with applications to clustering10.1016/j.patcog.2010.09.013 ↗
47. Cuturi, M., and Blondel, M. (2017, January 6–11). Soft-DTW: A differentiable loss function for time-series. Proceedings of the 34th International Conference on Machine Learning, ICML, Sydney, Australia.
48. Tslearn, a Machine Learning Toolkit for Time Series Data
49. Scikit-learn: Machine Learning in Python
50. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013, January 23). API design for machine learning software: Experiences from the scikit-learn project. Proceedings of the ECML PKDD Workshop: Languages for Data Mining and Machine Learning, Prague, Czech Republic.
51. Array programming with NumPy10.1038/s41586-020-2649-2 ↗
52. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python10.1038/s41592-019-0686-2 ↗
53. McKinney, W. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA.
54. Matplotlib: A 2D graphics environment10.1109/MCSE.2007.55 ↗
55. Development of a software application to evaluate the performance and energy losses of grid-connected photovoltaic systems10.1016/j.enconman.2014.02.026 ↗
56. Järvelä, M., and Valkealahti, S. (2020). Operation of a PV power plant during overpower events caused by the cloud enhancement phenomenon. Energies, 13.10.3390/en13092185 ↗
57. Optimal inverter sizing considering cloud enhancement10.1016/j.solener.2011.10.012 ↗
58. Enhanced values of global irradiance due to the presence of clouds in Eastern Mediterranean10.1016/j.renene.2013.08.001 ↗
59. Zehner, M., Weigl, T., Hartmann, M., Thaler, S., Schrank, O., Czakalla, M., Mayer, B., Betts, T.R., Gottschalg, R., and Behrens, K. (2011, January 5–8). Energy Loss Due to Irradiance Enhancement. Proceedings of the 26th European Photovoltaic Solar Energy Conference and Exhibition, Hamburg, Germany.
60. A study of extreme overirradiance events for solar energy applications using NASA’s I3RC Monte Carlo radiative transfer model10.1016/j.solener.2015.10.014 ↗
61. Espinosa-Gavira, M.J., Agüera-Pérez, A., de la Rosa, J.J., Palomares-Salas, J.C., and Sierra-Fernández, J.M. (2018). An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems. Sensors, 18.10.3390/s18103405 ↗