Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Al-Messabi, Naji; Goh, Cindy; El-Amin, Ibrahim; Li, Yun (2014)
Languages: English
Types: Other
Among renewable generators, photovoltaics (PV) is showing an increasing suitability and a lowering cost. However, integration of renewable energy sources possesses many challenges, as the intermittency of these non-conventional sources often requires generation forecast, planning and optimal management. There exists scope to improve present PV yield forecasting models and methods. For example, the popular dynamic neural network modelling method suffers from the lack of a selection mechanism for an optimal network structure. This paper develops an enhanced network for short-term forecasting of PV power yield, termed a `focused time-delay neural network' (FTDNN). The problem of optimizing the FTDNN structure is reduced to optimizing the number of delay steps and the number of neurons in the hidden layer alone and this problem is conveniently solved through heuristics. Two such algorithms, a genetic algorithm and particle swarm optimization (PSO) have been tested and both prove efficient and can improve the forecasting accuracy of the dynamic network. Given the success of the PSO in solving this discontinuous structural optimization problem, it is expected that PSO offers potential in optimizing both the structure and parameters of a forecasting model.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [17] P. Bacher, H. Madsen, H. A. Nielsen, “Online short-term solar power forecasting,” Solar Energy, Vol. 83, pp. 1772-1783, 2009.
    • [18] J. A. Gow, C. D. Manning, “Development of a Photovoltaic Array Model for use in Power-Electronics Simulation Studies,” IEE proceedings on Electric Power Applications, Vol. 146, no. 2, pp. 193- 200, March 1999.
    • [19] W. Xiao, M. G. J. Lind, W. G. Dunford, and A. Capel, “Real-time Identification of Optimal Operating Points in Photovoltaic Power Systems,” IEEE Transactions on Industrial Electronics, Vol. 53, No. 4, August 2006.
    • [20] M. G. Villalva, J. R. Gazoli, E. R. Filho, “Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays,” IEEE Transactions on Power Electronics, Vol. 24, No. 5, May 2009.
    • [21] B. Kroposki, K. Emery, D. Myers, and L. Mrig, “A Comparison of Photovoltaic Module Performance Evaluation Methodologies For Energy Ratings,” 24th IEEE Photovoltaic Conference, pp. 858-862, 1994.
    • [22] C. Craggs, E. M. Conway, N. M. Pearsall, “Statistical Investigation of the Optimal Averaging Time for Solar irradiance on Horizontal and Vertical Surfaces in the UK,” Solar Energy, Vol. 68, No. 2, pp. 179-187, 2000.
    • [23] B. H. Chowdhury, S. Rahman, "Is central station photovoltaic power dispatchable?," IEEE Transactions on Energy Conversion, Vol.3, No.4, pp.747-754, Dec. 1988.
    • [24] T. Hiyama and K. Kitabayashi, “Neural Network Based Estimation of Maximum Power Generation from PV Module using Environmental Information,” IEEE Trans. On Energy Conversion, Vol. 12, No. 3, 1997.
    • [25] A. Mellit, A. M. Pavan, “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, Vol. 84, pp. 807-821, 2010.
    • [26] Caputo, D.; Grimaccia, F.; Mussetta, M.; Zich, R.E., "Photovoltaic plants predictive model by means of ANN trained by a hybrid evolutionary algorithm," The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-6, 18-23 July 2010.
    • [27] A. Yona, T. Senjyu, A. Y. Saber, T. Funabashi, H. Sekine, K. ChulHwan, "Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System," International Conference on Intelligent Systems Applications to Power Systems , ISAP 2007, pp.1-6, 5-8 Nov. 2007.
    • [28] L. A. Fernandez-Jimenez, A. Munoz-Jimenez, A. Flaces, M, MendozVillena, E. Garcia-Garrido, P. Lara-Santillan, E. Zorzano-Alba, J. Zorzano-Santamaria, “Short-term Power Forecasting System for Photovoltaic plants,” Renewable Energy, Vol. 44, pp. 311-317, 2012.
    • [29] M. Rawat, K. Rawat, F. Ghannouchi, “Adaptive Digital Predistortion of Wireless Power Amplifiers/Transmitters using Dynamic Real-Valued Focused Time-Delay Line Neural Networks,” IEEE Transactions on Microwave Theory and Techniques, vol. 58, No. 1, Jan. 2010.
    • [30] K. Y. Lee and F. F. Yang, “Optimal reactive power planning using evolutionary algorithms: A comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming,” IEEE Trans. on PWRS, vol. 13, no. 1, pp. 101-108, 1998.
    • [31] R. Eberhat and Y. Shi, Particle Swarm Optimization: Developments, applications, and resources, Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, 2001, pp. 81-86.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article