http://iet.metastore.ingenta.com
1887

Improving combined solar power forecasts using estimated ramp rates: data-driven post-processing approach

Improving combined solar power forecasts using estimated ramp rates: data-driven post-processing approach

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Several forecasting models are combined together to mitigate the uncertainty associated with the solar power generation resource and improve the power generation forecasts. The common ensemble approach in wind and solar power forecasting is the blending of meteorological data from several sources. In this study, the present and the past solar power forecasts, as well as the associated meteorological data, are incorporated into an ensemble learning tool. Since forecasts based on numerical weather prediction systems are more valuable in horizons longer than 6 h, the proposed approach includes the simple persistence model of hour-ahead forecasts along with the different models of day-ahead forecasts so that the combined forecasts become hour-ahead solar power forecasts. In addition, the proposed approach combines the ramp rates of the forecasts to enhance the ensemble learning. Furthermore, the approach improves the ensemble learning by using two loss functions – the first function to minimise errors of the forecasts, and the second to minimise errors of the ramp rates of the forecasts. The performance of the combined forecasts is evaluated over the entire year and compared with other techniques.

References

    1. 1)
      • 1. Morales, J.M., Conejo, A.J., Madsen, H., et al: ‘Integrating renewables in electricity markets – operational problems, vol. 205 of international series in operations research & management Science’ (Springer US, Boston, MA, 2014).
    2. 2)
      • 2. Tuohy, A., Zack, J., Haupt, S.E., et al: ‘Solar forecasting: methods, challenges, and performance’, IEEE Power Energy Mag., 2015, 13, (6), pp. 5059.
    3. 3)
      • 3. Hong, T., Pinson, P., Fan, S., et al: ‘Probabilistic energy forecasting: global energy forecasting competition 2014 and beyond’, Int. J. Forecast., 2016, 32, (3), pp. 896913.
    4. 4)
      • 4. Voyant, C., Muselli, M., Paoli, C., et al: ‘Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation’, Energy, 2012, 39, (1), pp. 341355.
    5. 5)
      • 5. Aguiar, L.M., Pereira, B., Lauret, P., et al: ‘Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting’, Renew. Energy, 2016, 97, pp. 599610.
    6. 6)
      • 6. Marquez, R., Pedro, H.T.C., Coimbra, C.F.M.: ‘Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs’, Sol. Energy, 2013, 92, pp. 176188.
    7. 7)
      • 7. Liu, J.: ‘Combining sister load forecasts’, 2015.
    8. 8)
      • 8. Zamo, M., Mestre, O., Arbogast, P., et al: ‘A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production. Part I: deterministic forecast of hourly production’, Sol. Energy, 2014, 105, pp. 804816.
    9. 9)
      • 9. Huang, J., Troccoli, A., Coppin, P.: ‘An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records’, Renew. Energy, 2014, 72, pp. 195202.
    10. 10)
      • 10. Mohammed, A.A., Yaqub, W., Aung, Z.: ‘Probabilistic forecasting of solar power: an ensemble learning approach’, in Neves Silva, R., Jain, L.C., Howlett, R.J. (Eds.): ‘Intelligent decision technologies’ (Springer, Cham, 2015), pp. 449458.
    11. 11)
      • 11. Pierro, M., Bucci, F., De Felice, M., et al: ‘Multi-Model ensemble for day ahead prediction of photovoltaic power generation’, Sol. Energy, 2016, 134, pp. 132146.
    12. 12)
      • 12. Sperati, S., Alessandrini, S., Delle Monache, L.: ‘An application of the ECMWF ensemble prediction system for short-term solar power forecasting’, Sol. Energy, 2016, 133, (August), pp. 437450.
    13. 13)
      • 13. Pierro, M., Bucci, F., Cornaro, C., et al: ‘Model output statistics cascade to improve day ahead solar irradiance forecast’, Sol. Energy, 2015, 117, pp. 99113.
    14. 14)
      • 14. Cheung, W., Zhang, J., Florita, A., et al: ‘Ensemble solar forecasting statistical quantification and sensitivity analysis’. 5th Solar Integration Workshop, Brussels, vol. 1, 2015.
    15. 15)
      • 15. Lu, S., Youngdeok, H., Khabibrakhmanov, I., et al: ‘Machine learning based multi-physical-model blending for enhancing renewable energy forecast - improvement via situation dependent error correction’. 2015 European Control Conf., Linz, 2015, pp. 283290.
    16. 16)
      • 16. Catalão, J.P.S.: ‘Smart and sustainable power systems: operations, planning, and economics of insular electricity grids’ (CRC Press, Boca Raton, 2015).
    17. 17)
      • 17. Haupt, S., Kosovic, B.: ‘Variable generation power forecasting as a big data problem’, IEEE Trans. Sustain. Energy, 2016, 8, (2), pp. 725732.
    18. 18)
      • 18. Chu, Y., Pedro, H.T.C., Li, M., et al: ‘Real-time forecasting of solar irradiance ramps with smart image processing’, Sol. Energy, 2015, 114, pp. 91104.
    19. 19)
      • 19. Florita, A., Hodge, B.-M., Orwig, K.: ‘Identifying wind and solar ramping events’. 2013 IEEE Green Technologies Conf., Denver, 2013, pp. 147152.
    20. 20)
      • 20. Hirata, Y., Aihara, K.: ‘Predicting ramps by integrating different sorts of information’, Eur. Phys. J. Spec. Top., 2016, 225, (3), pp. 513525.
    21. 21)
      • 21. Reno, M.J., Hansen, C.W.: ‘Identification of periods of clear sky irradiance in time series of GHI measurements’, Renew. Energy, 2016, 90, pp. 520531.
    22. 22)
      • 22. Kleissl, J.: ‘Solar energy forecasting and resource assessment’ (Elsevier, Waltham, 2013).
    23. 23)
      • 23. Bacher, P., Madsen, H., Nielsen, H. A.: ‘Online short-term solar power forecasting’, Sol. Energy, 2009, 83, (10), pp. 17721783.
    24. 24)
      • 24. Hastie, T., Tibshirani, R., Friedman, J., et al: ‘The elements of statistical learning’ (Springer-Verlag, New York, 2009, 2nd edn.).
    25. 25)
      • 25. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, (1), pp. 532.
    26. 26)
      • 26. Hossain, M.R., Oo, A.M.T., Ali, A.: ‘The effectiveness of feature selection method in solar power prediction’, J. Renew. Energy, 2013, 2013, pp. 19.
    27. 27)
      • 27. Abuella, M., Chowdhury, B.: ‘Solar power probabilistic forecasting by using multiple linear regression analysis’. IEEE Southeastcon Proc., Fort Lauderdale, 2015.
    28. 28)
      • 28. Abuella, M., Chowdhury, B.: ‘Solar power forecasting using artificial neural networks’. North American Power Symp. (NAPS), Charlotte, NC, USA, 2015.
    29. 29)
      • 29. Abuella, M., Chowdhury, B.: ‘Random forest ensemble of support vector regression models for solar power forecasting’. 2017 IEEE Power Energy Society Innovative Smart Grid Technologies Conf. (ISGT), Arlington, April 2017, pp. 15.
    30. 30)
      • 30. Hong, T.: ‘Short term electric load forecasting’ (North Carolina State University, Raleigh, 2010).
    31. 31)
      • 31. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: ‘A practical guide to support vector classification’, BJU Int., 2008, 101, (1), pp. 13961400.
    32. 32)
      • 32. Pedro, H.T., Coimbra, C.F.: ‘Assessment of forecasting techniques for solar power production with no exogenous inputs’, Sol. Energy, 2012, 86, (7), pp. 20172028.
    33. 33)
      • 33. Mathiesen, P., Kleissl, J.: ‘Evaluation of numerical weather prediction for intraday solar forecasting in the continental United States’, Sol. Energy, 2011, 85, (5), pp. 967977.
    34. 34)
      • 34. Inman, R. H., Pedro, H. T., Coimbra, C. F.: ‘Solar forecasting methods for renewable energy integration’, Prog. Energy Combust. Sci., 2013, 39, (6), pp. 535576.
    35. 35)
      • 35. Antonanzas, J., Osorio, N., Escobar, R., et al: ‘Review of photovoltaic power forecasting’, 2016, 136, pp. 78111.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2017.0447
Loading

Related content

content/journals/10.1049/iet-rpg.2017.0447
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address