Citation: Darwish, H.H.; Al-Quraan, A. Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF. Sustainability 2023, 15, 3270. https://doi.org/10.3390/su15043270 Academic Editor: Byungik Chang Received: 11 January 2023 Revised: 6 February 2023 Accepted: 8 February 2023 Published: 10 February 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). sustainability Article Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF Hiba H. Darwish and Ayman Al-Quraan * Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan * Correspondence: aymanqran@yu.edu.jo Abstract: Estimating wind energy at a specific wind site depends on how well the real wind data in that area can be represented using an appropriate distribution function. In fact, wind sites differ in the extent to which their wind data can be represented from one region to another, despite the widespread use of the Weibull function in representing the wind speed in various wind locations in the world. In this study, a new probability distribution model (normal PDF) was tested to implement wind speed at several wind locations in Jordan. The results show high compatibility between this model and the wind resources in Jordan. Therefore, this model was used to estimate the values of the wind energy and the extracted energy of wind turbines compared to those obtained by the Weibull PDF. Several artificial intelligence techniques were used (GA, BFOA, SA, and a neuro-fuzzy method) to estimate and predict the parameters of both the normal and Weibull PDFs that were reflected in conjunction with the actual observed data of wind probabilities. Afterward, the goodness of fit was decided with the aid of two performance indicators (RMSE and MAE). Surprisingly, in this study, the normal probability distribution function (PDF) outstripped the Weibull PDF, and interestingly, BFOA and SA were the most accurate methods. In the last stage, machine learning was used to classify and predict the error level between the actual probability and the estimated probability based on the trained and tested data of the PDF parameters. The proposed novel methodology aims to predict the most accurate parameters, as the subsequent energy calculation phases of wind depend on the proper selection of these parameters. Hence, 24 classifier algorithms were used in this study. The medium tree classifier shows the best performance from the accuracy and training time points of view, while the ensemble-boosted trees classifier shows poor performance regarding providing correct predictions. Keywords: wind estimation; normal PDF; Weibull PDF; optimization algorithms; machine learning; prediction; classification; accuracy 1. Introduction Long ago, it was understood that the continuous usage of conventional energy sources (fossil fuel) jeopardizes and threatens the stability of life. As a result, humanity has tried to find other inexhaustible energy resources to tackle the issues of the undesired impacts of the dominant energy sources (fossil fuel). Renewable energy sources were the best alternative, which became grist, an integral part, and the interesting core of the energy sector due to their immense valuable features [1]. Furthermore, the lack of conventional energy resources boosts the harnessing of clean energy sources [2]. Inasmuch, the development of lifestyle is associated with energy demand. As such, the larger the energy demand in a certain area, the most sophisticated the area [3–7]. Wide choices of renewable energy are available, such as solar, the internal heat of the earth, wind, tidal, and biomass energy [8]. Wind energy has played a prominent, astounding, and marvelous role in contributing to the depreciation of carbon dioxide [9], Sustainability 2023, 15, 3270. https://doi.org/10.3390/su15043270 https://www.mdpi.com/journal/sustainability