Adaptive Neuro-fuzzy Inference System Based Short Term Wind Speed Forecasting
V.Vanitha1, D.Magdalin Mary2, G.Sophia Jasmine3, Akhil Balagopalan4
1V.Vanitha*, Department of Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore, India.
2D. Magdalin Mary, Department of Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore, India.
3G.Sophia Jasmine, Department of Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore, India.
4Akhil Balagopalan, Department of Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 389-391 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2252039520/2020©BEIESP | DOI: 10.35940/ijitee.E2252.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to the stochastic nature of wind speed, accurate wind power prediction plays a major challenge to power system operators for unit commitment and load dispatching. To predict wind power production with great accuracy, wind speed forecasting in different time horizons is gaining importance nowadays. This paper explores the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to forecast the wind speed in Logan international airport, USA for one year in every one hour time interval. ANFIS with different structures and membership functions are trained to find out the best model to do short term wind forecasting. Simulation with the best model is performed in MATLAB and the results show that the three input model with wind speed, direction and air pressure as inputs using Gaussian bell membership function provides the smallest errors.
Keywords: ANFIS, Logan airport, MAPE, Wind speed
Scope of the Article: Fuzzy logics