Long-term wind speed prediction using artificial neural network-based approaches

Manogaran Madhiarasan; Manogaran Madhiarasan

doi:10.3934/geosci.2021031

AIMS Geosciences

2021, Volume 7, Issue 4: 542-552. doi: 10.3934/geosci.2021031

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Long-term wind speed prediction using artificial neural network-based approaches

Manogaran Madhiarasan ^,

Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

Received: 17 August 2021 Accepted: 22 September 2021 Published: 24 September 2021

In the current scenario, worldwide renewable energy systems receive renewed interest because of the global reduction of greenhouse gas emissions. This paper proposes a long-term wind speed prediction model based on various artificial neural network approaches such as Improved Back-Propagation Network (IBPN), Multilayer Perceptron Network (MLPN), Recursive Radial Basis Function Network (RRBFN), and Elman Network with five inputs such as wind direction, temperature, relative humidity, precipitation of water content and wind speed. The proposed ANN-based wind speed forecasting models help plan, integrate, and control power systems and wind farms. The simulation result confirms that the proposed Recursive Radial Basis Function Network (RRBFN) model improves the wind speed prediction accuracy and minimizes the error to a minimum compared to other proposed IBPN, MLPN, and Elman Network-based wind speed prediction models.
- renewable energy systems,
- artificial neural networks,
- hidden neurons,
- wind speed,
- prediction
Citation: Manogaran Madhiarasan. Long-term wind speed prediction using artificial neural network-based approaches[J]. AIMS Geosciences, 2021, 7(4): 542-552. doi: 10.3934/geosci.2021031

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Abstract

In the current scenario, worldwide renewable energy systems receive renewed interest because of the global reduction of greenhouse gas emissions. This paper proposes a long-term wind speed prediction model based on various artificial neural network approaches such as Improved Back-Propagation Network (IBPN), Multilayer Perceptron Network (MLPN), Recursive Radial Basis Function Network (RRBFN), and Elman Network with five inputs such as wind direction, temperature, relative humidity, precipitation of water content and wind speed. The proposed ANN-based wind speed forecasting models help plan, integrate, and control power systems and wind farms. The simulation result confirms that the proposed Recursive Radial Basis Function Network (RRBFN) model improves the wind speed prediction accuracy and minimizes the error to a minimum compared to other proposed IBPN, MLPN, and Elman Network-based wind speed prediction models.

References

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