For proper and efficient utilization of wind power, the prediction of wind speed is very important. Wind is one of the main sources of energy in the world, but the wind turbines have a lack of reliability, continuity and homogeneity in power production. On the other hand, sudden changes of wind speed, lead to risk for wind turbine units health. Therefore, the prediction of wind speed for turbine maintenance and planning for production is very important. This paper provides a new method for predicting the wind speed. The technique is based on combining genetic algorithm and neural network. The previous wind speed information is used as inputs to Long-Step prediction (multi-day) of the wind speed. The proposed method was tested based on actual data collected from the MAPNA Co wind farm. Simulation results show the accuracy of the proposed model in predicting the wind speed. The accuracy of prediction models, based on root mean squared error (RMSE), is 0.96 meters per second. The results of the recurrent neural network genetic algorithm (RNNGA) method were compared with some reference methods which this model with less input data (wind speed), has the same or better accuracy.
Refan, M. H., Dameshghi, A., & Kamarzarrin, M. (2023). Wind Speed Long â Step Prediction based on RNNGA ybride Method. Energy Engineering and Management, 6(2), 2-11.
MLA
Mohammad Hossein Refan; Adel Dameshghi; Mehrnoosh Kamarzarrin. "Wind Speed Long â Step Prediction based on RNNGA ybride Method", Energy Engineering and Management, 6, 2, 2023, 2-11.
HARVARD
Refan, M. H., Dameshghi, A., Kamarzarrin, M. (2023). 'Wind Speed Long â Step Prediction based on RNNGA ybride Method', Energy Engineering and Management, 6(2), pp. 2-11.
VANCOUVER
Refan, M. H., Dameshghi, A., Kamarzarrin, M. Wind Speed Long â Step Prediction based on RNNGA ybride Method. Energy Engineering and Management, 2023; 6(2): 2-11.
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