A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems

Saira Al-Zadjali, Ahmed Al Maashri, Amer Al-Hinai, Rashid Al Abri, Swaroop Gajare, Sultan Al Yahyai and Mostafa Bakhtvar
Additional contact information
Saira Al-Zadjali: Department of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Ahmed Al Maashri: Department of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Amer Al-Hinai: Department of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Rashid Al Abri: Department of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
Swaroop Gajare: Sustainable Energy Research Center, Sultan Qaboos University, Al Khodh 123, Oman
Sultan Al Yahyai: Information and Technology, Mazoon Electricity Company, Fanja 600, Oman
Mostafa Bakhtvar: Sustainable Energy Research Center, Sultan Qaboos University, Al Khodh 123, Oman

Energies, 2021, vol. 14, issue 23, 1-20

Abstract: To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.

Keywords: ensemble neural networks; nowcasting; renewable energy; wind direction prediction; wind speed prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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