Liu, H., Wu, H. and Li, Y. (2018) Smart Wind Speed Forecasting Using EWT Decomposition, GWO Evolutionary Optimization, RELM Learning and IEWT Reconstruction. Energy Conversion and Management, 161, 266-283.
https://doi.org/10.1016/j.enconman.2018.02.006

has been cited by the following article:

• TITLE: Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

  AUTHORS: Bhuvana Ramachandran, Anbazhagan Swaminathan

  KEYWORDS: Wind Speed Prediction, Multiple Linear Regression, Grey Wolf Optimizer, Accuracy of Results, Wind Power

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.11 No.7, July 19, 2023

  ABSTRACT: Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature.