Forecasting models of wind power in Northeastern of Brazil

• Published in: The 2013 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
• Main Authors: de Aquino, Ronaldo R. B., Ludermir, Teresa B., Nobrega Neto, Otoni, Ferreira, Aida A., Lira, Milde M. S., Carvalho, Manoel A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2013
• Subjects: Artificial neural networks; Forecasting; Predictive models; Wind forecasting; Wind power generation; Wind speed
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN.2013.6706840

Wind Power forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast Brazil, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However, using wind power to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. This paper presents actual results of wind power forecasting for two parks in the region of northeastern Brazil with four different models. Models that perform power generation forecasting using the forecasted wind speeds and the wind power curve of the park are called Wind to Power (W2P) and models that perform power generation forecasting using the historical power generation of the park are called Power to Power (P2P). The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. Models that directly predict the wind power (P2P) got the best results. These models were more suitable for use in the power systems operation planning considering the wind parks analyzed in northeastern Brazil.