Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration

  • Diana Manjarres
  • Ricardo Alonso
  • Sergio Gil-Lopez
  • Itziar Landa-Torres
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10691)


Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.


Solar energy Renewable energy integration Optimization PV energy forecast 



This work has been supported in part by the ELKARTEK program of the Basque Government (BID3ABI project), and EMAITEK funds granted by the same institution.


  1. 1.
    Golestaneh, F., Pinson, P., Gooi, H.B.: Very short-term nonparametric probabilistic forecasting of renewable energy generation-with application to solar energy. IEEE Trans. Power Syst. 31(5), 3850–3863 (2016). CrossRefGoogle Scholar
  2. 2.
    Boland, J., David, M., Lauret, P.: Short term solar radiation forecasting: Island versus continental sites. Energy 113(15), 186–192 (2016)CrossRefGoogle Scholar
  3. 3.
    Join, C., Fliess, M., Voyant, C., Chaxel, F.: Solar energy production: short-term forecasting and risk management. IFAC (International Federation of Automatic Control) 49(12), 686–691 (2016)Google Scholar
  4. 4.
    Severiano, C., Guimaraes, F.G., Cohen, M.W.: Very short-term solar forecasting using multi-agent system based on extreme learning machines and data clustering. In: IEEE Symposium Series on Computational Intelligence (SSCI), Athens (2016).
  5. 5.
    Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Solar Energy 83(10), 1772–1783 (2009)CrossRefGoogle Scholar
  6. 6.
    Chen, C., Duan, S., Cai, T., Liu, B.: Online 24-h solar power forecasting based on weather type classification using artificial neural network. Solar Energy 85(11), 2856–2870 (2011)CrossRefGoogle Scholar
  7. 7.
    Hocaoglu, F.O., Gerek, Ö.N., Kurban, M.: Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Solar Energy 82(8), 714–726 (2008)CrossRefGoogle Scholar
  8. 8.
    Marquez, R., Coimbra, C.F.: Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy 85(5), 746–756 (2011)CrossRefGoogle Scholar
  9. 9.
    Pedro, H.T., Coimbra, C.F.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy 86(7), 2017–2028 (2012)CrossRefGoogle Scholar
  10. 10.
    Cao, J., Lin, X.: Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks. Energy Convers. Manag. 49(6), 1396–1406 (2008)CrossRefGoogle Scholar
  11. 11.
    Mellit, A., Pavan, A.M.: 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 84(5), 807–821 (2010)CrossRefGoogle Scholar
  12. 12.
    Mulder, G., De Ridder, F., Six, D.: Electricity storage for grid-connected household dwellings with PV panels. Solar energy 84(7), 1284–1293 (2010)CrossRefGoogle Scholar
  13. 13.
    Luthander, R., Widén, J., Nilsson, D., Palm, J.: Photovoltaic self-consumption in buildings: a review. Appl. Energy 142, 80–94 (2015)CrossRefGoogle Scholar
  14. 14.
    Widén, J.: Improved photovoltaic self-consumption with appliance scheduling in 200 single-family buildings. Appl. Energy 126, 199–212 (2014)CrossRefGoogle Scholar
  15. 15.
    Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid 4(4), 2364–2373 (2013)CrossRefGoogle Scholar
  16. 16.
    Chen, X., Wei, T., Hu, S.: Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home. IEEE Trans. Smart Grid 4(2), 932–941 (2013)CrossRefGoogle Scholar
  17. 17.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Diana Manjarres
    • 1
  • Ricardo Alonso
    • 1
  • Sergio Gil-Lopez
    • 1
  • Itziar Landa-Torres
    • 1
  1. 1.TECNALIA Research and InnovationDerioSpain

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