{"title":"卡拉奇机场风速资料的地统计估计和时间分布","authors":"Muhammad Irfan, Salman Zubair, Abid Hussain","doi":"10.53560/ppasa(58-3)627","DOIUrl":null,"url":null,"abstract":"Karachi city is a coastal city near the Arabian sea. Due to its location, wind speed may provide a sustainable small scale wind energy system as well as reduction in power shortage in the city. In this study, wind speed data of Karachi Airport station at 10m height is used to estimate wind speed in the surrounding area with reference to measured wind speed data of the station. This estimated wind speed will then be helpful to assess small scale wind power generation at the unsampled locations. Geo-Statics tool in ArcGIS version 10.1 software was utilised to estimate measured wind speed using different interpolation methods. Descriptive statistics were used to analyse and compare measured and estimated wind speed. The analysis summarises the effectiveness of the estimated wind speed. Time series variations of the wind speed data was also analysed. Temporal mapping showing seasonal variations of the wind speed. The descriptive statistics illustrated a high value of correlation coefficient ‘r’, coefficient of determination R2 which is 99.8% for Ordinary and Universal kriging interpolation methods, while it was calculated 99.6% for Simple kriging. A slightly higher coefficient of variation resulted in Ordinary and Universal kriging methods than the Simple kriging method. The results indicated that all three kriging methods performed better and are more effective to estimate wind speed and wind power in the surrounding area and for Temporal display.","PeriodicalId":36961,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: Part A","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geo-Statistical Estimation and Temporal Distribution of Wind Speed Data of Karachi Airport\",\"authors\":\"Muhammad Irfan, Salman Zubair, Abid Hussain\",\"doi\":\"10.53560/ppasa(58-3)627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Karachi city is a coastal city near the Arabian sea. Due to its location, wind speed may provide a sustainable small scale wind energy system as well as reduction in power shortage in the city. In this study, wind speed data of Karachi Airport station at 10m height is used to estimate wind speed in the surrounding area with reference to measured wind speed data of the station. This estimated wind speed will then be helpful to assess small scale wind power generation at the unsampled locations. Geo-Statics tool in ArcGIS version 10.1 software was utilised to estimate measured wind speed using different interpolation methods. Descriptive statistics were used to analyse and compare measured and estimated wind speed. The analysis summarises the effectiveness of the estimated wind speed. Time series variations of the wind speed data was also analysed. Temporal mapping showing seasonal variations of the wind speed. The descriptive statistics illustrated a high value of correlation coefficient ‘r’, coefficient of determination R2 which is 99.8% for Ordinary and Universal kriging interpolation methods, while it was calculated 99.6% for Simple kriging. A slightly higher coefficient of variation resulted in Ordinary and Universal kriging methods than the Simple kriging method. The results indicated that all three kriging methods performed better and are more effective to estimate wind speed and wind power in the surrounding area and for Temporal display.\",\"PeriodicalId\":36961,\"journal\":{\"name\":\"Proceedings of the Pakistan Academy of Sciences: Part A\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Pakistan Academy of Sciences: Part A\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53560/ppasa(58-3)627\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Physics and Astronomy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Pakistan Academy of Sciences: Part A","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53560/ppasa(58-3)627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Physics and Astronomy","Score":null,"Total":0}
引用次数: 0
摘要
卡拉奇市是阿拉伯海附近的一个沿海城市。由于它的位置,风速可以提供一个可持续的小规模风能系统,并减少城市的电力短缺。本研究使用卡拉奇机场站10m高度的风速数据,参照该站实测风速数据估算周边风速。估算的风速将有助于评估未采样地点的小规模风力发电。利用ArcGIS version 10.1软件中的geostatics工具,采用不同的插值方法对实测风速进行估算。描述性统计用于分析和比较测量风速和估计风速。分析总结了估算风速的有效性。分析了风速数据的时间序列变化。显示风速季节变化的时间图。描述性统计表明,普通克里格插值法和通用克里格插值法的相关系数r和决定系数R2为99.8%,而简单克里格插值法的相关系数R2为99.6%。普通克里格法和通用克里格法的变异系数略高于简单克里格法。结果表明,三种kriging方法均能较好地估计周边地区的风速和风力,并能较好地进行时态显示。
Geo-Statistical Estimation and Temporal Distribution of Wind Speed Data of Karachi Airport
Karachi city is a coastal city near the Arabian sea. Due to its location, wind speed may provide a sustainable small scale wind energy system as well as reduction in power shortage in the city. In this study, wind speed data of Karachi Airport station at 10m height is used to estimate wind speed in the surrounding area with reference to measured wind speed data of the station. This estimated wind speed will then be helpful to assess small scale wind power generation at the unsampled locations. Geo-Statics tool in ArcGIS version 10.1 software was utilised to estimate measured wind speed using different interpolation methods. Descriptive statistics were used to analyse and compare measured and estimated wind speed. The analysis summarises the effectiveness of the estimated wind speed. Time series variations of the wind speed data was also analysed. Temporal mapping showing seasonal variations of the wind speed. The descriptive statistics illustrated a high value of correlation coefficient ‘r’, coefficient of determination R2 which is 99.8% for Ordinary and Universal kriging interpolation methods, while it was calculated 99.6% for Simple kriging. A slightly higher coefficient of variation resulted in Ordinary and Universal kriging methods than the Simple kriging method. The results indicated that all three kriging methods performed better and are more effective to estimate wind speed and wind power in the surrounding area and for Temporal display.