Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

IEEJ Transactions on Electrical and Electronic Engineering

Volumn 6, Issue 6, 2011, Pages 571-576

  Pousinho, Hugo M I ^a,b   Mendes, Victor M F ^c   Catalão, João P S ^a,b  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c INSTITUTO SUPERIOR DE ENGENHARIA DE LISBOA   (Portugal)

Author keywords

Forecasting; Fuzzy logic; Neural networks; Wind power

Indexed keywords

FORECASTING; FUZZY LOGIC; FUZZY NEURAL NETWORKS; WIND POWER;

ADAPTIVE NEURO-FUZZY INFERENCE; ELECTRIC GRIDS; FORECASTING TOOLS; INTERMITTENCY; NUMERICAL RESULTS; PORTUGAL; SHORT-TERM FORECASTING; WIND POWER FORECASTING;

FUZZY INFERENCE;

EID: 80054741197     PISSN: 19314973     EISSN: 19314981     Source Type: Journal    
DOI: 10.1002/tee.20697     Document Type: Article

References (27)

1. Ackermann T (ed). Wind Power in Power Systems. John Wiley & Sons: West Sussex; 2005.
2. Melício R, Mendes VMF, Catalão JPS. A pitch control malfunction analysis for wind turbines with permanent magnet synchronous generator and full-power converters: proportional integral versus fractional-order controllers. Electric Power Components and Systems 2010; 38(4):387-406.
3. Ernst B, Oakleaf B, Ahlstrom ML, Lange M, Moehrlen C, Lange B, Focken U, Rohrig K. Predicting the wind. IEEE Power and Energy Magazine 2007; 5(6):78-89.
4. Costa A, Crespo A, Navarro J, Lizcano G, Madsen H, Feitosa E. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 2008; 12(6):1725-1744.
5. Taniguchi K, Ichiyanagi K, Yukita K, Goto Y. Study on forecast of time series of wind velocity for wind power generation by using wide meteorological data. IEEJ Transactions on Power and Energy 2008; 128(2):416-422.
6. Ma L, Luan SY, Jiang CW, Liu HL, Zhang Y. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 2009; 13(4):915-920.
7. Kavasseri RG, Seetharaman K. Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy 2009; 34(5):1388-1393.
8. Catalão JPS, Pousinho HMI, Mendes VMF. An artificial neural network approach for short-term wind power forecasting in Portugal. Engineering Intelligent Systems for Electrical Engineering and Communications 2009; 17(1):5-11.
9. Ramirez-Rosado IJ, Fernandez-Jimenez LA, Monteiro C, Sousa J, Bessa R. Comparison of two new short-term wind-power forecasting systems. Renewable Energy 2009; 34(7):1848-1854.
10. Mabel MC, Fernandez E. Estimation of energy yield from wind farms using artificial neural networks. IEEE Transactions on Energy Conversion 2009; 24(2):459-464.
11. Yona A, Senjyu T, Urasaki N, Funabshi T. Application of recurrent neural network to 3-hours-ahead generating power forecasting for wind power generators. IEEJ Transactions on Power and Energy 2009; 129(5):591-597.
12. Kakuta S, Wu G. A study of forecasting precision enhancement of short-term wind power generation by use of NN method. IEEJ Transactions on Power and Energy 2009; 129(9):1091-1097.
13. Jursa R, Rohrig K. Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. International Journal of Forecasting 2008; 24(4):694-709.
14. Bessa RJ, Miranda V, Gama J. Entropy and correntropy against minimum square error in offline and online three-day ahead wind power forecasting. IEEE Transactions on Power Systems 2009; 24(4):1657-1666.
15. Fan S, Liao JR, Yokoyama R, Chen LN, Lee WJ. Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Transactions on Energy Conversion 2009; 24(2):474-482.
16. Fujimura N, Yasuno T, Yakushiji R, Takigawa K, Kawasaki K. Simple wind power prediction system using self-tuning fuzzy reasoning and error persistent model. IEEE Transactions on Power Systems 2009; 129(5):614-620.
17. Moreno J. Hydraulic plant generation forecasting in Colombian power market using ANFIS. Energy Economics 2009; 31(3):450-455.
18. Ata R, Kocyigit Y. An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Systems with Applications 2010; 37(7):5454-5460.
19. Mellit A, Arab AH, Khorissi N, Salhi H. An ANFIS-based forecasting for solar radiation data from sunshine duration and ambient temperature. Proceedings of the 2007 IEEE Power Engineering Society General Meeting, Tampa, Florida, USA, 2007.
20. Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Transactions on Power Systems 2008; 82(2):853-858.
21. Wu J-D, Hsu C-C, Chen H-C. An expert system of price forecasting for used cars using adaptive neuro-fuzzy inference. Expert Systems with Applications 2009; 36(4):7809-7817.
22. Mahmoud TS, Marhaban MH, Hong TS. ANFIS: self-tuning fuzzy PD controller for twin rotor MIMO system. IEEJ Transactions on Power and Energy 2010; 5(3):369-371.
23. Catalão JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM. Short-term electricity prices forecasting in a competitive market: a neural network approach. Electric Power Systems Research 2007; 77(10):1297-1304.
24. Rodriguez CP, Anders GJ. Energy price forecasting in the Ontario competitive power system market. IEEE Transactions on Power Systems 2004; 19(1):366-374.
25. Ying LC, Pan MC. Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversion and Management 2008; 49(2):205-211.
26. Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 1993; 82(2):665-685.
27. Conejo AJ, Plazas MA, Espínola R, Molina AB. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power Systems 2005; 20(2):1035-1042.