A Meteorological–Statistic Model for Short-Term Wind Power Forecasting

Author:

Lima João MarcosORCID,Guetter Alexandre K.,Freitas Saulo R.,Panetta Jairo,de Mattos João G. Z.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications,Energy Engineering and Power Technology,Control and Systems Engineering

Reference26 articles.

1. Ahlstrom, M. L., & Zavadil, R. M. (2005). The role of wind forecasting in grid operations and reliability. In Proceedings of IEEE/PES transmission and distribution conference and exhibition: Asia and Pacific, China (pp. 1–5). doi: 10.1109/TDC.2005.1547203 .

2. DOE-Department of Energy (US). (2008). 20% Wind energy by 2030: Increasing wind energy’s contribution to U.S. electricity supply DOE office of energy efficiency and renewable energy report. Available at: http://www.ipd.anl.gov/anlpubs/2009/11/65613.pdf . Accessed May 16, 2017.

3. DOE-Department of Energy (US). (2009). Wind power forecasting: State-of-the-art 2009—Decision and Information Sciences Division—Argonne National Laboratory—U.S. Department of Energy. Available at: http://www.ipd.anl.gov/anlpubs/2009/11/65613.pdf . Accessed May 16, 2017.

4. EPE and MME (Empresa de Pesquisas Energéticas e Ministério de Minas Energia). (2015). Plano Decenal de Expansão de Energia 2024. Available at: http://www.epe.gov.br/PDEE/Relat%C3%B3rio%20Final%20do%20PDE%202024.pdf . Accessed May 16, 2017.

5. Freitas, S. R., Longo, K. M., Dias, M. A. F. S., Chatfield, R., Dias, P. S., Artaxo, P., et al. (2009). The coupled aerosol and tracer transport model to the Brazilian developments on the regional atmospheric modeling system (CATT-BRAMS)—Part 1: Model description and evaluation. Atmospheric Chemistry and Physics, 9(8), 2843–2861. doi: 10.5194/acp-9-2843-2009 .

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