Iran has a lot of renewable and nonrenewable energy resources. Since Iran has a special geographic position, it has a lot of solar and wind energy resources. Both solar and wind energy are free, renewable and adaptable with environment. The study of 10-year-wind data in Iranian synoptic stations shows that the production of wind power electricity is possible in many regions of Iran such as the Coast of Oman Sea, Persian Gulf islands, coastal areas of Khozestan province, the Eastern area of Iran, and stations of Manjil, Rafsanjan, Ardebil and Bijar. Also the production of wind power electricity is possible in many other regions of Iran during limited time. According to the findings of this study, Synoptic stations are divided into four different groups in terms of wind speed. The first group has great wind power most of the year, the second group has great wind power in some parts of the day sometime in the year, the third has wind power in limited times during the year and the fourth group does not have any significant wind power throughout the year.

Investigating of the lateral force on structures under wind loading is very important, especially in the analysis and design of wind-sensitive structures and structures located in windy areas. In the building design regulations, the basic wind speed in each area is presented as a basic parameter for the initial estimation of the amount of load on the structures. Due to the lack of provision of the basic wind speed in the latest edition of the sixth topic of the National Building Regulations for the region of Namin city and considering the establishment of meteorological stations in the aforesaid region. In the present work, the wind speed data was investigated in terms of probabilistic distribution and the determination of the return period. The results show that the prevailing wind direction in Namin region is from the east. The maximum annual wind speed is 17.7 m/s and the average annual wind speed is 30 m/s. Also, Weibull, Log-Normal, and Gamble distribution functions have the highest compatibility with the investigated data, respectively. The basic wind speed of Ardabil station is 38.9 m/s according to the latest edition of the sixth topic of the National Building Regulations (which is the closest station to Namin city area used in the analysis and design calculations of structures under wind loading). Based on the data available in Namin city, the basic wind speed is 31.28 m/s with a return period of 50 years, and it is less than the value mentioned in the sixth topic of the National Building Regulations.

Nowadays, increasing the renewable energy applications in power system, especially wind power, has caused higher imbalance probability between generation and demand. Therefore, an accurate estimateion of wind farm reserve requirements and the reserve cost reduction in power systems with high wind power penetration is very important. In this paper, the reserve requirements of a wind farm is estimated by using a probabilistic approach. Reserve requirements of wind farm are divided into two categories provided by fast-responsive and slow-responsive resources. Indeed the purpose of this division is decreasing the cost of reserve provision by reducing the use of fast-responsive resources that is more expensive in comparison with slow-responsive resources. Wind speed prediction has been done by the ANN (Artificial Neural Network) and ARIMA (Autoregressive Integrated Moving Average), with using real measured data on a wind farm in the state of Pennsylvania. In this study, the Reserve requirements of wind farms and the cost of provision of reserve requirements will be reduced by using artificial neural networks that is a method based on artificial intelligence and is more accurate than statistical and traditional method ARIMA.

Wind energy is a renewable energy source that has grown rapidly in recent decades. This energy is converted into electricity using advanced INVELOX wind turbines. However, the wind velocity is critical, and predicting this velocity in real-time is challenging. As a result, a deep learning (DL) model has been developed to predict the velocity in advanced wind turbines using a novel enhanced Long Short-Term Memory (LSTM) model. The LSTM enhancement is executed by employing the Black Widow optimization with Mayfly optimization in the Python platform as application software. The dataset has been prepared using Ansys Fluent fluid flow analysis. In addition to that, the wind turbine power generation was computed analytically. A subsonic wind tunnel test is also performed by employing a 3-Dimensional printed physical model to validate the simulation dataset for this innovative design. The proposed MFBW-LSTM model (Enhanced LSTM with BWO and MFO) predicts efficiently, with an accuracy of 95. 34%. Furthermore, the performance of the proposed model is compared to LSTM, BW-LSTM, and MF-LSTM. Accuracy, MAE, MAPE, MSE, and RMSE are among the performance criteria the proposed DL model achieves efficiently. As a result, the proposed DL model is best suited for velocity prediction of an Advanced INVELOX wind turbine in various cross sections with high accuracy.

In this study, the mean monthly wind speed and wind energy potential for 11 synoptic stations in Isfahan province have been calculated and analyzed on the basis of hourly wind speed data over the climatic period of 1992-2005. Mean monthly wind power has been estimated by fitting hybrid Weibull and inverse Gaussian distributions to hourly wind speed data and also by using direct method. The results showed that the wind speed during the cold months (November, December and January) is lower than the other months.With regard to the onset of early spring (April), the wind speed increases gradually over the area and decreasing trend starts in September. Estimated wind energy for selected stations showed that during the period of low wind speed, the wind power density has declined to below 60 w/m2. The Wind energy power increases due to the increase of wind speed with the onset of February to the point that it amounts to 60 w/m2 in Ardestan, Naien, Kabootarabad and to over 140 w/m2 in Shahreza. Wind energy power in association with decreasing of wind speed decreases after April, except in Ardestan. Generally, among the stations in the region, Khorbiabanak, Daran and Natanz are envisaged with low wind speed and wind energy. The pattern of monthly wind power in Ardestan is different with respect to the other stations.

Introduction: Nowadays, the exploitation of the renewable energy sources such as wind plays a key role in human life. Although, Iran has a high potential for wind power generation, there is not an efficient energy planning yet. Environmental variables such as wind speed have variations according to spatial points. It seems reasonable to consider that there exists a spatial correlation between wind speed data at different locations. In geostatistics the spatial autocorrelation of data could be investigated by calculating the experimental semivariogram. The parameters of the fitted semivariogram model may be used to estimate the wind speed at unknown locations through kriging algorithms....

In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of statistical energy analysis. Correct estimation of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound estimation.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.