# Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm

arXiv: https://arxiv.org/abs/2002.06226 · HTML: https://mlsanity.com/paper/2002.06226/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature](https://mlsanity.com/paper/2202.12256/) (2022)
- [Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit](https://mlsanity.com/paper/2204.05476/) (2022)
- [Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate](https://mlsanity.com/paper/2204.08459/) (2022)
- [Multi-Step Short-Term Wind Speed Prediction with Rank Pooling and Fast Fourier Transformation](https://mlsanity.com/paper/2211.14434/) (2022)
- [A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model for Variable Terrain Conditions](https://mlsanity.com/paper/2408.15554/) (2024)

## Abstract (arXiv)

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI, and KGE parameters. It was concluded that the WOA optimization algorithm can improve the prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.