Climate change is one of the most essential phenomena studied by several researchers in the last few decades. The main reason this phenomenon occurs is greenhouse gases (GHG), chiefly CO2 emissions. About 30% of the created GHG emissions are achieved by electricity generation. This article investigates the role of renewable energy projects in Jordan, specifically wind and solar energy, in mitigating climate change and water consumption reduction using RETScreen software. It was found that the cumulative water consumption reduction from 2017 to 2021 due to the use of wind and solar projects is equal to 6.9491 × 109 gallons. Finally, the results show that the future dependence on renewable energy projects in Jordan to meet the growth in demand by the year 2030 reduces the expected increment in the climate temperature by 1.047 °C by that year.
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.
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