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Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul

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Abstract

This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM10, PM2.5, and SO2 are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

The codes generated during the current study are available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial Neural Network

AOD:

Aerosol Optical Depth

ARIMA:

Autoregressive Integrated Moving Average

BSA:

Black Sky Albedo

CART:

Classification and Regression Tree

CC:

Cloud Coverage

CNN:

Convolutional Neural Network

COD:

Cloud Optical Depth

DT:

Decision Tree

ELM:

Extreme Learning Machine

GBRT:

Gradient Boosted Regression Tree

GCM:

Global Circulation Model

IFS:

Integrated Forecasting System

IQR:

Interquartile Range

LSTM:

Long Short Term Memory

MAE:

Mean Absolute Error

MARS:

Multivariate Adaptive Regression Spline

MBE:

Mean Bias Error

MLR:

Multiple Linear Regression

MSE:

Mean Square Error

NN:

Neural Networks

nRMSE:

Normalized Root Mean Square Error

NWP:

Numerical Weather Prediction

O3 :

Total ozone layer

PDP:

Partial Dependence Plot

PM10:

Particulate matter with a diameter of 10 microns or less

PM2.5:

Particulate matter with a diameter of 2.5 microns or less

PREC:

Precipitation

PRES:

Pressure

PSO:

Particle Swarm Optimization

PV:

Photovoltaic

RF:

Random Forest

RMSE:

Root Mean Square Error

RSS:

Residual Sum of Squares

RT:

Random Tree

RTM:

Radiative Transfer Models

SO2:

Sulphur dioxide

SSR:

Surface Solar Radiation

SVR:

Support Vector Regression

Temp:

Temperature

u10:

Eastward Wind Speed

v10:

Northward Wind Speed

WRF:

Weather Research and Forecasting Model

ZA:

Zenith Angle

References

  • AWG Radiation Budget Application Team. (2018). GOES-R Advanced Baseline Imager (ABI) algorithm theoretical basis document for downward shortwave radiation (surface), and reflected shortwave radiation (TOA). NOAA NESDIS Center for Satellite Applications and Research.

    Google Scholar 

  • Basílio, S. D. C. A., Putti, F. F., Cunha, A. C., & Goliatt, L. (2023). An evolutionary-assisted machine learning model for global solar radiation prediction in Minas Gerais region, southeastern Brazil. Earth Science Informatics. https://doi.org/10.1007/s12145-023-00990-0

    Article  Google Scholar 

  • Bhattacharjee, A. D., & Chowdhury, A. R. (2022). Short-term solar irradiance fore-casting using long short term memory variants. In Proceedings of international conference on data science and applications (pp. 227–243). Springer.

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees (1st ed.). CRC Press. https://doi.org/10.1201/9781315139470

    Book  Google Scholar 

  • Chen, F., Zhou, Z., Lin, A., Niu, J., Qin, W., & Yang, Z. (2019). Evaluation of direct horizontal irradiance in China using a physically-based model and machine learning methods. Energies, 12(1), 150. https://doi.org/10.3390/en12010150

    Article  Google Scholar 

  • Chen, J., Zhu, W., & Yu, Q. (2021). Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest. Renewable Energy, 178, 916–929. https://doi.org/10.1016/j.renene.2021.06.129

    Article  Google Scholar 

  • Chen, Y., Bai, M., Zhang, Y., Liu, J., & Yu, D. (2023). Error revision during morning period for deep learning and multi-variable historical data-based day-ahead solar irradiance forecast: Towards a more accurate daytime forecast. Earth Science Informatics. https://doi.org/10.1007/s12145-023-01026-3

    Article  Google Scholar 

  • Copernicus Climate Change Service (C3S). (2021). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS).

    Google Scholar 

  • Deo, R. C., & Şahin, M. (2017). Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renewable and Sustainable Energy Reviews, 72, 828–848. https://doi.org/10.1016/j.rser.2017.01.114

    Article  Google Scholar 

  • EMBER. (2022). Global electricity review 2022. https://ember-climate.org/app/uploads/2022/03/Report-GER22.pdf

  • Fan, J., Wu, L., Ma, X., Zhou, H., & Zhang, F. (2020). Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions. Renewable Energy, 145, 2034–2045. https://doi.org/10.1016/j.renene.2019.07.104

    Article  CAS  Google Scholar 

  • Feng, Y., & Li, Y. (2018). Estimated spatiotemporal variability of total, direct and diffuse solar radiation across China during 1958–2016. International Journal of Climatology, 38(12), 4395–4404. https://doi.org/10.1002/joc.5676

    Article  Google Scholar 

  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189–1232.

    Article  Google Scholar 

  • Gareth, J., Daniela, W., Trevor, H., & Robert, T. (2013). An introduction to statistical learning: With applications in R. Springer.

    Google Scholar 

  • Ghimire, S., Deo, R. C., Raj, N., & Mi, J. (2019). Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction. Renewable and Sustainable Energy Reviews, 113, 109247. https://doi.org/10.1016/j.rser.2019.109247

    Article  Google Scholar 

  • Gürel, A. E., Ağbulut, Ü., & Biçen, Y. (2020). Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation. Journal of Cleaner Production, 277, 122353. https://doi.org/10.1016/j.jclepro.2020.122353

    Article  Google Scholar 

  • Hai, T., Sharafati, A., Mohammed, A., Salih, S. Q., Deo, R. C., Al-Ansari, N., & Yaseen, Z. M. (2020). Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access, 8, 2026–12042. https://doi.org/10.1109/ACCESS.2020.2965303

    Article  Google Scholar 

  • Hartmann, D. L., Tank, A. M. K., Rusticucci, M., Alexander, L. V., Brönnimann, S., Charabi, Y. A. R., Dentener, F. J., Dlugokencky, E. J., Easterling, D. R., Kaplan, A., & Soden, B. J. (2013). Observations: atmosphere and surface. In Climate change 2013 the physical science basis: Working group I contribution to the fifth assessment report of the intergovernmental panel on climate change (pp. 159–254). Cambridge University Press.

  • Hocaoğlu, F. O., Gerek, Ö. N., & Kurban, M. (2008). Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Solar Energy, 82(8), 714–726. https://doi.org/10.1016/j.solener.2008.02.003

    Article  Google Scholar 

  • Hou, N., Zhang, X., Zhang, W., Wei, Y., Jia, K., Yao, Y., Jiang, B., & Cheng, J. (2020). Estimation of surface downward shortwave radiation over China from Himawari-8 AHI data based on Random Forest. Remote Sensing, 12(1), 181. https://doi.org/10.3390/rs12010181

    Article  Google Scholar 

  • Jiang, Y. (2008). Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models. Energy Policy, 36(10), 3833–3837. https://doi.org/10.1016/j.enpol.2008.06.030

    Article  Google Scholar 

  • Jiang, B., Liang, S., Ma, H., Zhang, X., Xiao, Z., Zhao, X., Jia, K., Yao, Y., & Jia, A. (2016). GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation. Remote Sensing, 8(3), 222. https://doi.org/10.3390/rs8030222

    Article  Google Scholar 

  • Kisi, O., Heddam, S., & Yaseen, Z. M. (2019). The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model. Applied Energy, 241, 184–195. https://doi.org/10.1016/j.apenergy.2019.03.089

    Article  Google Scholar 

  • Lam, J. C., Wan, K. K., & Yang, L. (2008). Solar radiation modelling using ANNs for different climates in China. Energy Conversion and Management, 49(5), 1080–1090. https://doi.org/10.1016/j.enconman.2007.09.021

    Article  Google Scholar 

  • Lima, F. J., Martins, F. R., Pereira, E. B., Lorenz, E., & Heinemann, D. (2016). Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renewable Energy, 87, 807–818. https://doi.org/10.1016/j.renene.2015.11.005

    Article  Google Scholar 

  • Luiz, E. W., Martins, F. R., Gonçalves, A. R., & Pereira, E. B. (2018). Analysis of intra-day solar irradiance variability in different Brazilian climate zones. Solar Energy, 167, 210–219. https://doi.org/10.1016/j.solener.2018.04.005

    Article  Google Scholar 

  • Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9

    Article  Google Scholar 

  • Martins, F. R., Pereira, E. B., & Guarnieri, R. A. (2012). Solar radiation forecast using artificial neural networks. International Journal of Energy Science, 2(6), 56–67.

    Google Scholar 

  • Mellit, A., Eleuch, H., Benghanem, M., Elaoun, C., & Pavan, A. M. (2010). An adaptive model for predicting of global, direct and diffuse hourly solar irradiance. Energy Conversion and Management, 51(4), 771–782. https://doi.org/10.1016/j.enconman.2009.10.034

    Article  Google Scholar 

  • Ministry of Energy and Natural Resources. (2023). Renewable energy. https://enerji.gov.tr/eigm-resources-en

  • Ministry of Environment, Urbanization and Climate Change. (2022). Air quality databank. https://sim.csb.gov.tr/

  • Mubiru, J., & Banda, E. J. K. B. (2008). Estimation of monthly average daily global solar irradiation using artificial neural networks. Solar Energy, 82(2), 181–187. https://doi.org/10.1016/j.solener.2007.06.003

    Article  Google Scholar 

  • Ohmura, A. (2009). Observed decadal variations in surface solar radiation and their causes. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2008JD011290

    Article  Google Scholar 

  • Rahimikhoob, A., Behbahani, S. M. R., & Banihabib, M. E. (2013). Comparative study of statistical and artificial neural network’s methodologies for deriving global solar radiation from NOAA satellite images. International Journal of Climatology, 33(2), 480–486. https://doi.org/10.1002/joc.3441

    Article  Google Scholar 

  • Ryu, Y., Jiang, C., Kobayashi, H., & Detto, M. (2018). MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000. Remote Sensing of Environment, 204, 812–825. https://doi.org/10.1016/j.rse.2017.09.021

    Article  Google Scholar 

  • Qin, J., Chen, Z., Yang, K., Liang, S., & Tang, W. (2011). Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products. Applied Energy, 88(7), 2480–2489. https://doi.org/10.1016/j.apenergy.2011.01.018

    Article  Google Scholar 

  • Qin, Y., Huang, J., McVicar, T. R., West, S., Khan, M., & Steven, A. D. (2021). Estimating surface solar irradiance from geostationary Himawari-8 over Australia: A physics-based method with calibration. Solar Energy, 220, 119–129. https://doi.org/10.1016/j.solener.2021.03.029

    Article  Google Scholar 

  • Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R., & Khovanova, N. (2019). Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomedical Signal Processing and Control, 52, 456–462. https://doi.org/10.1016/j.bspc.2017.01.012

    Article  Google Scholar 

  • Sharafati, A., Khosravi, K., Khosravinia, P., Ahmed, K., Salman, S. A., Yaseen, Z. M., & Shahid, S. (2019). The potential of novel data mining models for global solar radiation prediction. International Journal of Environmental Science and Technology, 16(11), 7147–7164. https://doi.org/10.1007/s13762-019-02344-0

    Article  Google Scholar 

  • Sianturi, Y., Sopaheluwakan, A., & Sartika, K. A. (2021). Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region. IOP Conference Series: Earth and Environmental Science, 893(1), 012074.

    Google Scholar 

  • Singla, P., Duhan, M., & Saroha, S. (2022). Solar irradiation forecasting by long-short term memory using different training algorithms. In Renewable energy optimization, planning and control (pp. 81–89). Springer.

  • Srivastava, R., Tiwari, A. N., & Giri, V. K. (2019). Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India. Heliyon, 5(10), e02692. https://doi.org/10.1016/j.heliyon.2019.e02692

    Article  Google Scholar 

  • Tang, W., Qin, J., Yang, K., Liu, S., Lu, N., & Niu, X. (2016). Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data. Atmospheric Chemistry and Physics, 16(4), 2543–2557. https://doi.org/10.5194/acp-16-2543-2016

    Article  CAS  Google Scholar 

  • Tymvios, F. S., Jacovides, C. P., Michaelides, S. C., & Scouteli, C. (2005). Comparative study of Ångström’s and artificial neural networks’ methodologies in estimating global solar radiation. Solar Energy, 78(6), 752–762. https://doi.org/10.1016/j.solener.2004.09.007

    Article  Google Scholar 

  • Vakitbilir, N., Hilal, A., & Direkoğlu, C. (2022). Hybrid deep learning models for multivariate forecasting of global horizontal irradiation. Neural Computing and Applications, 34, 8005–8026.

    Article  Google Scholar 

  • Voyant, C., Muselli, M., Paoli, C., & Nivet, M. L. (2011). Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation. Energy, 36(1), 348–359. https://doi.org/10.1016/j.energy.2010.10.032

    Article  Google Scholar 

  • Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569–582. https://doi.org/10.1016/j.renene.2016.12.095

    Article  Google Scholar 

  • Wang, L., Kisi, O., Zounemat-Kermani, M., Zhu, Z., Gong, W., Niu, Z., Liu, H., & Liu, Z. (2017). Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree. International Journal of Climatology, 37(3), 1141–1155. https://doi.org/10.1002/joc.4762

    Article  Google Scholar 

  • Wang, T., Yan, G., & Chen, L. (2012). Consistent retrieval methods to estimate land surface shortwave and longwave radiative flux components under clear-sky conditions. Remote Sensing of Environment, 124, 61–71. https://doi.org/10.1016/j.rse.2012.04.026

    Article  Google Scholar 

  • Wei, Y., Zhang, X., Hou, N., Zhang, W., Jia, K., & Yao, Y. (2019). Estimation of surface downward shortwave radiation over China from AVHRR data based on four machine learning methods. Solar Energy, 177, 32–46. https://doi.org/10.1016/j.solener.2018.11.008

    Article  Google Scholar 

  • Wild, M. (2009). Global dimming and brightening: A review. Journal of Geophysical Research: Atmospheres, 114(D10), D00D16.

    Article  Google Scholar 

  • Wild, M. (2012). Enlightening global dimming and brightening. Bulletin of the American Meteorological Society, 93(1), 27–37.

    Article  Google Scholar 

  • Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C. N., Dutton, E. G., Forgan, B., Kallis, A., Russak, V., & Tsvetkov, A. (2005). From dimming to brightening: Decadal changes in solar radiation at Earth’s surface. Science, 308(5723), 847–850. https://doi.org/10.1126/science.1103215

    Article  CAS  Google Scholar 

  • Willson, R. C., & Mordvinov, A. V. (2003). Secular total solar irradiance trend during solar cycles 21–23. Geophysical Research Letters. https://doi.org/10.1029/2002GL016038

    Article  Google Scholar 

  • Yang, L., Zhang, X., Liang, S., Yao, Y., Jia, K., & Jia, A. (2018). Estimating surface downward shortwave radiation over China based on the gradient boosting decision tree method. Remote Sensing, 10(2), 185. https://doi.org/10.3390/rs10020185

    Article  Google Scholar 

  • Yoon, J. (2021). Forecasting of real GDP growth using machine learning models: Gradient boosting and random forest approach. Computational Economics, 57(1), 247–265. https://doi.org/10.1007/s10614-020-10054-w

    Article  Google Scholar 

  • Zeng, Z., Wang, Z., Gui, K., Yan, X., Gao, M., Luo, M., Geng, H., Liao, T., Li, X., An, J., Liu, H., He, C., Ning, G., & Yang, Y. (2020). Daily global solar radiation in China estimated from high-density meteorological observations: a random forest model framework. Earth and Space Science, 7(2), e2019EA001058. https://doi.org/10.1029/2019EA001058

    Article  Google Scholar 

  • Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324. https://doi.org/10.1016/j.trc.2015.02.019

    Article  Google Scholar 

  • Zhang, Y., & Chen, L. (2022). Estimation of daily average shortwave solar radiation under clear-sky conditions by the spatial downscaling and temporal extrapolation of satellite products in mountainous areas. Remote Sensing, 14(11), 2710. https://doi.org/10.3390/rs14112710

    Article  Google Scholar 

  • Zhou, Q., Flores, A., Glenn, N. F., Walters, R., & Han, B. (2017). A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the US. PLoS ONE, 12(8), e0180239. https://doi.org/10.1371/journal.pone.0180239

    Article  CAS  Google Scholar 

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  1. ITU Ayazaga Campus, Eurasia Institute of Earth Sciences, Istanbul Technical University, 34469, Istanbul, Turkey

    Denizhan Guven

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Guven, D. Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul. Pure Appl. Geophys. (2024). https://doi.org/10.1007/s00024-024-03472-6

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