Historical weather data supported hybrid renewable energy forecasting using artificial neural network (ANN)

Energy Procedia

Volumn 14, Issue , 2012, Pages 1035-1040

  Hossain, Rahat ^a   Maung Than Oo, Amanullah ^a   Shawkat Ali, A B M ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

Feedforward network; Historical weather data; Hybrid forecasting; Neural network; Renewable energy

Indexed keywords

CLIMATE MODELS; HYBRID SYSTEMS; LEARNING ALGORITHMS; MATLAB; METEOROLOGY; NEURAL NETWORKS; SOLAR ENERGY;

CLIMATOLOGICAL PARAMETERS; FEED-FORWARD NETWORK; HISTORICAL WEATHER DATUM; HYBRID FORECASTING; HYBRID RENEWABLE ENERGIES; RENEWABLE ENERGIES; SHORT-TERM FORECASTING; SUBTROPICAL CLIMATES;

WEATHER FORECASTING;

EID: 84858386389     PISSN: 18766102     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.egypro.2011.12.1048     Document Type: Conference Paper

References (9)

1. Thor, S. E. and Weis Taylor, P. Long-term research and development needs for wind energy for the time frame 2000-2020, Wind Energy, 5:73-75, 2003.
2. L. Ma, S. Y. Luan, C. W. Jiang, H. L. Liu, and Y. Zhang. A review on the forecasting of wind speed and generated power. Renew. Sust. Energy Rev., vol. 13, pp. 915-920, May 2009.
3. I. J. Ramirez-Rosado, L. A. Fernandez-Jimenez, C. Monteiro, J. Sousa, and R. Bessa. Comparison of two new short-term wind-power forecasting systems. Renew. Energy, vol. 34, pp. 1848-1854, Jul. 2009.
4. G. Sideratos and N. D. Hatziargyriou. An advanced statistical method for wind power forecasting. IEEE Trans. Power Syst., vol. 22, pp. 258- 265, Feb. 2007.
5. C. W. Potter and W. Negnevitsky. Very short-term wind forecasting for Tasmanian power generation. IEEE Trans. Power Syst., vol. 21, pp. 965-972, May 2006.
6. R. Jursa, K. Rohrig. Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int. J. Forecast., vol. 24, pp. 694-709, Oct.-Dec. 2008.
7. T. H. M. El-Fouly, E. F. El-Saadany, and M. M. A. Salama. Improved grey predictor rolling models for wind power prediction. IET Gener, Transm, Distrib, vol. 1, pp. 928-937, Nov. 2007.
8. R. Bessa, V. Miranda, and J. Gama. Improvement in wind power forecasting based on information entropy-related concepts. Presented at the 2008 IEEE Power & Energy Society General Meeting, Pittsburgh, Pennsylvania. July, 2008.
9. U.Focken, M.Lange and H.P. Waldl. Previento: regional wind power prediction system with innovative upscaling algorithm. EWEC 2001, Copenhagen.