A new mathematical technique and its Python program to assess wind potential Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1186/s43088-024-00510-z
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.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s43088-024-00510-z
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- OpenAlex ID
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Raw OpenAlex JSON
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https://doi.org/10.1186/s43088-024-00510-zDigital Object Identifier
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A new mathematical technique and its Python program to assess wind potentialWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-06-22Full publication date if available
- Authors
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Shafiq Ur Rehman, Naeem Sadiq, Iqbal Tariq, Mahwish Mobeen Khan, Muhammad Mustaqeem Zahid, Ahmed Ali Rajput, Zaheer UddinList of authors in order
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https://doi.org/10.1186/s43088-024-00510-zPublisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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Python (programming language), Computer science, Programming languageTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.five | 81 |
| abstract_inverted_index.mean | 114, 117 |
| abstract_inverted_index.only | 157 |
| abstract_inverted_index.root | 113 |
| abstract_inverted_index.that | 163 |
| abstract_inverted_index.used | 11, 64, 104 |
| abstract_inverted_index.were | 102, 127 |
| abstract_inverted_index.wind | 36, 72, 110, 133, 137, 161, 167, 175, 189 |
| abstract_inverted_index.based | 5 |
| abstract_inverted_index.city. | 152 |
| abstract_inverted_index.eight | 40 |
| abstract_inverted_index.known | 82 |
| abstract_inverted_index.least | 179 |
| abstract_inverted_index.model | 106 |
| abstract_inverted_index.speed | 52, 176 |
| abstract_inverted_index.those | 160 |
| abstract_inverted_index.value | 172 |
| abstract_inverted_index.Akaike | 124 |
| abstract_inverted_index.Python | 19, 68 |
| abstract_inverted_index.almost | 148 |
| abstract_inverted_index.caters | 158 |
| abstract_inverted_index.cities | 41 |
| abstract_inverted_index.employ | 24 |
| abstract_inverted_index.energy | 90 |
| abstract_inverted_index.error, | 116, 119 |
| abstract_inverted_index.factor | 92 |
| abstract_inverted_index.method | 9, 87, 156 |
| abstract_inverted_index.speed. | 138 |
| abstract_inverted_index.speeds | 162 |
| abstract_inverted_index.square | 115 |
| abstract_inverted_index.values | 77, 131 |
| abstract_inverted_index.Lahore, | 48 |
| abstract_inverted_index.Multan, | 44 |
| abstract_inverted_index.Quetta, | 45 |
| abstract_inverted_index.Results | 17 |
| abstract_inverted_index.Weibull | 15, 33, 145 |
| abstract_inverted_index.average | 136, 171 |
| abstract_inverted_index.between | 141 |
| abstract_inverted_index.compare | 75, 130 |
| abstract_inverted_index.maximum | 94, 99, 188 |
| abstract_inverted_index.method, | 86, 93, 96, 101 |
| abstract_inverted_index.method. | 27, 186 |
| abstract_inverted_index.minutes | 60 |
| abstract_inverted_index.modeled | 144 |
| abstract_inverted_index.pattern | 91 |
| abstract_inverted_index.program | 20, 69 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Khuzdar, | 43 |
| abstract_inverted_index.absolute | 118 |
| abstract_inverted_index.approach | 4 |
| abstract_inverted_index.interval | 57 |
| abstract_inverted_index.methods, | 83 |
| abstract_inverted_index.modified | 98 |
| abstract_inverted_index.moments, | 89 |
| abstract_inverted_index.observed | 192 |
| abstract_inverted_index.recorded | 54, 142 |
| abstract_inverted_index.calculate | 71 |
| abstract_inverted_index.criterion | 126 |
| abstract_inverted_index.determine | 108 |
| abstract_inverted_index.developed | 22 |
| abstract_inverted_index.empirical | 85 |
| abstract_inverted_index.implement | 66 |
| abstract_inverted_index.potential | 37, 190 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Hyderabad. | 194 |
| abstract_inverted_index.Islamabad, | 47 |
| abstract_inverted_index.Peshawar). | 50 |
| abstract_inverted_index.calculated | 128 |
| abstract_inverted_index.contribute | 164 |
| abstract_inverted_index.likelihood | 95, 100 |
| abstract_inverted_index.parameters | 34, 134 |
| abstract_inverted_index.potential. | 73, 111 |
| abstract_inverted_index.potential; | 168 |
| abstract_inverted_index.therefore, | 169 |
| abstract_inverted_index.(Hyderabad, | 42 |
| abstract_inverted_index.Bahawalpur, | 46 |
| abstract_inverted_index.Conclusions | 153 |
| abstract_inverted_index.coefficient | 120 |
| abstract_inverted_index.correlation | 140 |
| abstract_inverted_index.implemented | 30 |
| abstract_inverted_index.information | 125 |
| abstract_inverted_index.parameters, | 80 |
| abstract_inverted_index.parameters. | 16 |
| abstract_inverted_index.Pakistan’s | 39 |
| abstract_inverted_index.Newton–Gauss | 8, 26 |
| abstract_inverted_index.determination, | 122 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5024165371 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I193670377 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.92943096 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |