评估风能潜力的新数学技术及其 Python 程序

IF 2.5 Q2 MULTIDISCIPLINARY SCIENCES Beni-Suef University Journal of Basic and Applied Sciences Pub Date : 2024-06-22 DOI:10.1186/s43088-024-00510-z
Shafiq Ur Rehman, Naeem Sadiq, Iqbal Tariq, Mahwish Mobeen Khan, Muhammad Mustaqeem Zahid, Ahmed Ali Rajput, Zaheer Uddin
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引用次数: 0

摘要

背景使用基于牛顿-高斯方法的新方法来求取威布尔参数。结果开发了一个 Python 程序来使用牛顿-高斯方法。该程序用于计算巴基斯坦八个城市(海德拉巴、胡兹达尔、木尔坦、奎达、巴哈瓦尔布尔、伊斯兰堡、拉合尔和白沙瓦)的 Weibull 参数和风能潜力。2016 年每十分钟记录一次的风速数据被用于执行 Python 程序来计算风能潜力。为了比较参数值,还使用了五种已知方法,即经验法、矩法、能量模式因子法、最大似然法和修正最大似然法,来模拟和确定风势。计算了均方根误差、平均绝对误差、判定系数和 Akaike 信息准则,以比较风参数值和平均风速。在每个城市,记录的 Weibull pdf 与建模的 Weibull pdf 之间的相关性几乎达到 99%。海得拉巴的风势最大。
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A new mathematical technique and its Python program to assess wind potential

Background

A new approach based on the Newton–Gauss method is used to find the Weibull parameters.

Results

A Python program was developed to employ the Newton–Gauss method. It is implemented to find Weibull parameters and wind potential of Pakistan’s eight cities (Hyderabad, Khuzdar, Multan, Quetta, Bahawalpur, Islamabad, Lahore, and Peshawar). Wind speed data recorded at an interval of ten minutes for 2016 is used to implement a Python program to calculate wind potential. To compare the values of the parameters, five known methods, the empirical method, method of moments, energy pattern factor method, maximum likelihood method, and modified maximum likelihood method, were also used to model and determine the wind potential. The root mean square error, mean absolute error, coefficient of determination, and Akaike information criterion were calculated to compare values of wind parameters and average wind speed. The correlation between recorded and modeled Weibull pdf was almost 99% for each city.

Conclusions

The new method only caters to those wind speeds that contribute to the wind potential; therefore, the average value of the wind speed is the least in the case of the new method. The maximum wind potential was observed for Hyderabad.

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期刊介绍: Beni-Suef University Journal of Basic and Applied Sciences (BJBAS) is a peer-reviewed, open-access journal. This journal welcomes submissions of original research, literature reviews, and editorials in its respected fields of fundamental science, applied science (with a particular focus on the fields of applied nanotechnology and biotechnology), medical sciences, pharmaceutical sciences, and engineering. The multidisciplinary aspects of the journal encourage global collaboration between researchers in multiple fields and provide cross-disciplinary dissemination of findings.
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