The main methodology in every Wind power prediction model involves converting Wind speed into power using the power output curve of the Wind turbine. However, preceding studies that have introduced models for such curves have not considered the impact of Wind direction and its recurring fluctuations over time on predicting Wind turbine power output. The main focus of these studies has just been on the magnitude of Wind speed and the relationship between Wind speed and turbine power. The present study models the effect of Wind direction on Wind turbine power output and uses it to modify the quadratic power curve equations. Using these modified equations and considering the turbine mechanism to follow the Wind direction, a method is presented for predicting Wind turbine power output under frequent changes in Wind direction over time. To deal with the lack of access to long term and high-resolution Wind data, registered historical data and probabilistic distribution functions are used to produce lost data with software. To demonstrate the efficacy of the suggested approach, the real data recorded for a 1.5 MW turbine installed in Khaf in Razavi Khorasan, Iran, are used as a case study. Finally, the potential Wind power and potential income of the four Windy regions in Iran were assessed based on the payment mechanism of the Organization of Renewable Energy and Electricity Efficiency of Iran, assuming the same installed capacity. The effect of Wind direction and its variations over time, which can affect power output of Wind turbine and income, is the main focus of this section of paper.

BACKGROUND AND OBJECTIVES: Overusing renewable resources for various purposes is making it necessary to use fewer non-renewable ones to generate energy. Finding alternative renewable energy sources is essential for energy production. This study concentrated on using Wind direction and speed to produce Wind energy among renewable energy sources. Data on Wind direction and speed were statistically analyzed to determine the current distribution pattern, which is then used to project the amount of Wind energy that will be available in the future.METHODS: This study concentrated on choosing Wind direction and speed to minimize the potential for current electricity generation from Wind turbines, using data collected between 1981 and 2023. The Wind speed and direction distribution pattern was assessed through the Weibull distribution, beta distribution, and three-parameter Weibull distribution. The Anderson-Darling test and the Kolmogorov-Smirnov test were employed in this study to determine the goodness-of-fit of a specific distribution. The forecasting analysis was expanded from 2024 to 2050 based on the three-parameter Weibull distribution and Anderson-Darling test results for future sustainable Wind energy production.FINDINGS: The average Wind speed was found to be 6.51 meters per second, with a standard deviation of 0.280 meters per second between 1981 and 2023. The Wind direction varied between a minimum of 3.56 and a maximum of 356.44 degrees for the same duration. The study discovered that the three-parameter Weibull distribution caused less error in the Wind speed data distribution pattern than both the Weibull distribution and beta distribution, based on the results of the Anderson-Darling and Kolmogorov-Smirnov tests. From both the tests on Weibull distribution, beta distribution, and three-parameter Weibull distribution, this study found that the Anderson-Darling test was the most appropriate for forecasting the Wind speed corresponding to the Wind direction for the periods between 2024 and 2050 to produce sustainable Wind energy from the Wind turbine.CONCLUSION: The outcomes of this study demonstrate that there is a good likelihood that the parameter Weibull distribution and Anderson-Darling test will be used in other nations to aid in the complementary integration of Wind energy. This research has the potential to significantly reduce the amount of environmentally hazardous energy sources used to meet societal requirements. This study offers a trustworthy technique for assessing Wind direction and speed, which helps design sustainable Wind power plants, construct engineering curricula, and estimate clean, environmentally friendly energy sources.

The direction and speed of Winds play an important role in the occurrence of pollutions and dust stormas well as location placement for the establishment of industries and the expansion of cities. Yazd is a dry area of Iran, and constantly exposed to the dust particles in the air; and this in terms of human life and health issues are of special importance. In this paper a day when due to the amount of dust particles visual acuity reduces to 5, 000 m is considered the dusty day. In this study dusty days and direction and Wind speed of 4 synoptic stations were studied in spring 2010. Pearson correlation has been used in the study of relationship between Wind direction and speed and the occurrence of dust. The results show a direct correlation between Wind direction and speed, the Wind direction nearer to 360o, (North) Wind speed increases. There is an inverse correlation between the speed and the visibility in dusty days, where in 17 cases this relationship has been significant up to confidence level. Of course, in 5 cases the calculated correlation is negative. So according to the obtained results. there is an inverse relation between speed and visibility in stormy days, so that with increased Wind speed visibility is reduced. Although the test does not show a significant correlation between Wind speed and Visibility, due to a direct correlation between Wind speed and Wind direction from one side and the reverse relationship between two components of speed and visibility it can be said that whatever Wind direction gets close to the North, in high speed Winds visibility reduces.

This article presents a new method for detecting heterogeneities in Wind data set to predict Wind speed based on the well-known Hidden Markov Model (HMM). In the proposed method, the HMM categorizes the Wind time series into some groups in which each group represents a Wind regime. Each regime uses an internal first-order Markov Chain (MC) for forecasting, and the combination of all regimes outputs generates the final Wind speed forecast. The model proposed in this study is called “Hierarchical Markov Model ”. The first layer detects and separates Wind regimes as heterogenic groups of Wind data by the use of Wind direction data, based on  HMM, and the second layer forecasts the Wind speed using MC. The proposed model is implemented and tested using real data. Its effectiveness in terms of temporal stationary index is compared with that of a first-order MC-based method. The results showed that more than 70% improvement can be achieved in Wind speed prediction by the proposed method. Moreover, it gives a probability distribution function of Wind speed prediction, which is sharper than the one obtained with the first-order MC; means that more precise prediction

Introduction: Dust is a natural process in desert areas, which is caused by the effect of strong Winds on the surface of the soil. The aim of this study is to investigate the conditions of the formation and expanding the dust storm formed in Iraq to the Khuzestan province in February, 2017. Materials and methods: The synoptic analysis was conducted using meteorological parameters from ERA-interim and observation data from the Ahwaz station. In addition, the EUMETSAT image, the DREAM and the HYSPLIT model output were used. Results: As the results show, regarding to the storm, dust in low-pressure area located over Iran is expanded toward the west associated with the tough 500 hPa. With the formation of the high-pressure area in Syria, a pressure gradient is formed in Iraq resulting the northwest Wind with the Wind speed of 28 knots s (14 m/s) in 925 hPa. These conditions have led to transport the dust from Iraq toward southwest of Iran. A jet stream is formed 12 h before entering the dust from Iraq to Iran, at 900 hPa. The HYSPLIT model predicts the particle’ s motion along the northwest Winds, and is matched with the Wind field in synoptic structure. Conclusion: According to this study, the most important factors in dust transport to the Persian Gulf are geographic location of the low-and high-pressure area and the formed northwest Winds in Iraq.

The issue of pedestrian-level Wind environments around high-rise buildings is closely related to the comfort and safety of human settlements. In this paper, we study the effects of different Wind direction angles and spacing ratios on the Wind environment at pedestrian heights around buildings arranged in an equilateral triangle configuration. Three-dimensional steady-state numerical simulation was employed, with the standard k-ε model selected as the turbulence model. Wind speed ratios and different area ratio parameters are used to quantitatively express the degree and range of influence of Wind speed by buildings. The results show that the maximum Wind speed ratio at the corner of a building is greatly affected by the Wind direction angle, with 45°, 135°, and 157.5° being the unfavorable Wind direction angles. Conversely, the area ratio of different areas is greatly affected by the spacing ratio. As the spacing ratio increases, the mutual interference effect between buildings weakens, resulting in a better pedestrian Wind environment. Owing to the unique layout of the building group, different degrees of ventilation corridors are formed among the three buildings. The Wind speed amplification effect in the corridors is more significant, and the areas with poor Wind environments are concentrated in these corridors.