Jump to ContentJump to Main Navigation
Show Summary Details
More options …

International Journal of Emerging Electric Power Systems

Editor-in-Chief: Sidhu, Tarlochan

Ed. by Khaparde, S A / Rosolowski, Eugeniusz / Saha, Tapan K / Gao, Fei


CiteScore 2018: 0.86

SCImago Journal Rank (SJR) 2018: 0.220
Source Normalized Impact per Paper (SNIP) 2018: 0.430

Online
ISSN
1553-779X
See all formats and pricing
More options …
Volume 15, Issue 1

Issues

Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power

Antonio Bracale / Guido Carpinelli
  • Corresponding author
  • Department of Electrical Engineering and Information Technologies, Napoli Federico II University, Napoli, Italy
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Annarita Di Fazio / Shahab Khormali
Published Online: 2014-01-23 | DOI: https://doi.org/10.1515/ijeeps-2013-0131

Abstract

Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems’ losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

Keywords: renewable energy; photovoltaic power; forecasting methods; accuracy indices; cost-based indices; electricity market

References

  • 1.

    Bouhafs F, Mackay M, Merabti M. Links to the future. IEEE Power & Energy Magazine, January/February 2012.Google Scholar

  • 2.

    Potter CM, Archambault A, Kenneth W. Building a smarter smart grid to better renewable energy information. Power Systems Conference and Exposition (PSCE ’09), Seattle, WA, March 2009.Google Scholar

  • 3.

    Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system. IEEE Power Engineering Society General Meeting, Tampa, FL, June 2007.Google Scholar

  • 4.

    Lorenz E, Hurka J, Heinemann D, Beyer HG. Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Selected Top Appl Earth Observations Remote Sensing 2009; 2:2–10.CrossrefWeb of ScienceGoogle Scholar

  • 5.

    Li Y, He L, Nie R. Short-term forecast of power generation for grid-connected photovoltaic system based on advanced Grey-Markov chain. IEEE International Conference on Energy and Environment Technology (ICEET ’09), Guilin, China, October 2009.Google Scholar

  • 6.

    Bacher P, Madsen H, Nielsen P. Online short-term solar power forecasting. Solar Energy 2009; 83:1772–83.Web of ScienceCrossrefGoogle Scholar

  • 7.

    Hassanzadeh M, Etezadi-Amoli M, Fadali MS. Practical approach for sub-hourly and hourly prediction of PV power output. IEEE Conferences North American Power Symposium, Arlington, TX, September 2010.Google Scholar

  • 8.

    Bracale A, Caramia P, Fantauzzi M, Di Fazio AR. A Bayesian-based approach for photovoltaic power forecast. Cigrè International Symposium on Smart Grid, Bologna, Italy, September 2011.Google Scholar

  • 9.

    Bracale A, Caramia P, De Martinis U, Di Fazio AR. An improved Bayesian-based approach for short term photovoltaic power forecasting in smart grids. Renewable Energy Power Qual J 2012; 10.Google Scholar

  • 10.

    Cococcioni M, Andrea ED, Lazzerini B. 24-hour-ahead forecasting of energy production in solar PV systems. International Conference Intelligent Systems Design and Applications (ISDA 2011), Cordoba, Spain, November 2011.Google Scholar

  • 11.

    Sudirman R, Ashnayi K, Golbaba M. Comparison of methods used for forecasting solar radiation. International Conference Green Technologies, Tulsa, OK, April 2012.Google Scholar

  • 12.

    Mellit A, Pavan AM. A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy 2010; 84:807–21.CrossrefWeb of ScienceGoogle Scholar

  • 13.

    Chaouachi A, Kamel RM, Ichikawa R, Havayashi H, Nagasaka K. Neural network ensemble-based solar power generation short-term forecasting. Adv Comput Intell Intell Inform 2010; 14:69–75.Google Scholar

  • 14.

    Bracale A, Caramia P, Carpinelli G, Di Fazio AR, Ferruzzi G. A Bayesian method for short-term probabilistic forecasting of photovoltaic generation in smart grid operation and control. Energies 2013; 6:733–47.CrossrefWeb of ScienceGoogle Scholar

  • 15.

    Changsong C, Duan S, Cai T, Liu B. Online 24-h solar power forecasting based on weather type classification using artificial neural network. Solar Energy 2011; 85:2856–70.Web of ScienceGoogle Scholar

  • 16.

    Pedro HT, Coimbra CF. Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy 2012; 86:2017–28.Web of ScienceCrossrefGoogle Scholar

  • 17.

    Changsong C, Cai T, Shanxu D. Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement. 2nd International Symposium on Power Electronics for Distributed Generation Systems, Hefei, China, June 2010.Google Scholar

  • 18.

    Martin L, Zarzalejo LF, Jesus P, Navarro A, Marchante R, Cony M. Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning. Solar Energy 2010; 84:1772–81.CrossrefWeb of ScienceGoogle Scholar

  • 19.

    Fernandez-Jimenez LA, Munoz-Jimenez A, Falces A, Mendoza-Villena M, Garcia-Garrido E, Santillan-Lara PM, et al.Short-term forecasting system for photovoltaic plants. Renewable Energy 2012; 44:311–7.Web of ScienceCrossrefGoogle Scholar

  • 20.

    Pelland S, Remund J, Kleissl J, Oozeki T, De Brabandere K. Photovoltaic and solar forecasting: state of the art. Report IEA-PVPs T14-01, October 2013.Google Scholar

  • 21.

    Pinson P, Juban J, Kariniotakis GN. On the quality and value of probabilistic forecasts of wind generation. PMAPS 2006, Stockholm, Sweden, June 2006.Google Scholar

  • 22.

    Holttinen H. Optimal electricity market for wind power. Energy Pol 2005; 33:2052–63.CrossrefGoogle Scholar

  • 23.

    Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. Int J Forecasting 2006; 22:679–88.CrossrefGoogle Scholar

  • 24.

    Gomez-Esposito A, Conejo AJ, Canizares C. Electric energy systems analysis and operation. Boca Raton, FL: CRC Press, 2009.Google Scholar

  • 25.

    De Gooijer JG, Hyndman RJ. 25 years of time series forecasting. Int J Forecasting 2006; 22:443–73.CrossrefGoogle Scholar

  • 26.

    Haykin S. Neural networks: a comprehensive foundation, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 1999.Google Scholar

  • 27.

    Yona A, Senjyu T, Yousuf A, Funabashi T, Sekine H, Chul-Hwan K. Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. Proceedings of the 14th International Conference on Intelligent System Applications to Power Systems, Kaohsiung, Taiwan, November 2007.Google Scholar

  • 28.

    Hoff TE, Perez R, Klessl J, Renne D, Stein J. Reporting of irradiance modeling relative prediction errors. Prog. Photovoltaics,2013; 21(7);1514–1519.CrossrefWeb of ScienceGoogle Scholar

About the article

Published Online: 2014-01-23


Citation Information: International Journal of Emerging Electric Power Systems, Volume 15, Issue 1, Pages 77–91, ISSN (Online) 1553-779X, ISSN (Print) 2194-5756, DOI: https://doi.org/10.1515/ijeeps-2013-0131.

Export Citation

©2014 by Walter de Gruyter Berlin / Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Yasuaki Miyazato, Shota Tobaru, Kosuke Uchida, Cirio Celestino Muarapaz, Abdul Motin Howlader, and Tomonobu Senjyu
Sustainability, 2017, Volume 9, Number 1, Page 117
[2]
Yasuaki Miyazato, Hayato Tahara, Kosuke Uchida, Cirio Celestino Muarapaz, Abdul Motin Howlader, and Tomonobu Senjyu
Sustainability, 2016, Volume 8, Number 12, Page 1273
[3]
Antonio Bracale, Guido Carpinelli, Pasquale De Falco, Renato Rizzo, and Angela Russo
Journal of Renewable and Sustainable Energy, 2016, Volume 8, Number 2, Page 023505

Comments (0)

Please log in or register to comment.
Log in