Geo-Statistical Estimation and Temporal Distribution of Wind Speed Data of Karachi Airport
Geo-Statistical Estimation and Temporal Distribution of Wind Speed of Karachi Airport
DOI:
https://doi.org/10.53560/PPASA(58-3)627Keywords:
Wind Speed Estimation, Geographical Information System (GIS), Geostatistical Analysis, Kriging Interpolation, Descriptive Statistics, Wind Power PotentialAbstract
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.
References
B. Yaniktepe, T. Koroglu, and M. Savrun. Investigation of wind characteristics and wind energy potential in Osmaniye, Turkey. Renewable and Sustainable Energy Reviews 21: 703-711 (2013).
J. Wu, J. Wang, and D. Chi. Wind energy potential assessment for the site of Inner Mongolia in China. Renewable and Sustainable Energy Reviews 21:215-228 (2013).
H. Chai, W. Cheng, C. Zhou, X. Chen, X. Ma, and S. Zhao. Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur Autonomous Region, China. Natural Science 3(12): 999 (2011).
F. Collins, and P. Bolstad. A comparison of spatial interpolation techniques in temperature estimation. In proceedings of the Third International Conference. Paper presented at the Workshop on Integrating GIS and Environmental Modeling, Santa Fe, New Mexico (1996).
P. Goovaerts. Performance comparison of geostatistical algorithms for incorporating elevation into the mapping of precipitation. Paper presented at the The IV International Conference on GeoComputation was hosted by Mary Washington College in Fredericksburg, VA, USA (1999).
D.L. Phillips., J. Dolph, and D. Marks. A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agricultural and forest meteorology 58(1-2): 119-141 (1992).
S.M. Serrano, M.A. Sánchez, and J. M. Cuadrat. Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Climate research 24(2): 161-180 (2003).
Q. H. Escobedo, R. S. Flores, E. R. García, and F.M. Agugliaro. Wind energy resource in Northern Mexico. Renewable and Sustainable Energy Reviews 32: 890-914 (2014).
Y. V. Yap. Estimation and Validation of Wind Speed by using Spatial Interpolation. Thesis B.Eng. (Hons.) Civil Engineering, University of Malaysia Pahang, Mayalsia (2015).
W. Luo, M. Taylor, and S. Parker. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International Journal of Climatology: A Journal of the Royal Meteorological Society 28(7): p. 947-959 (2008).
B. Cabrera. A geostatistical method for the analysis and prediction of air quality time series: application to the Aburrá Valley region. Master Thesis. Technical University of Munich, Germany (2016).
M.A. Ahmed, F. Ahmed, and M.W. Akhtar. Assessment of wind power potential for coastal areas of Pakistan. Turkish Journal of Physics 30(2): 127-135 (2006).
Q.Z. Chaudhry. An investigation on wind power potential of Gharo-Sindh, Pakistan. Pakistan journal of meteorology 6(11): (2009).
M.A. Chaudhry, R. Raza, and S.A. Hayat. Renewable energy technologies in Pakistan: prospects and challenges. Renewable and Sustainable Energy Reviews 13(6-7): 1657-1662 (2009).
M.A. Hussain, S. Abbas, M.R. Ansari, A. Zaffar, and B. Jan. Wind speed analysis of some coastal areas near Karachi. Pakistan Academy of Sciences (2012).
M.H. Baloch, G.S. Kaloi, and Z.A. Memon. Current scenario of the wind energy in Pakistan challenges and future perspectives: A case study. Energy Reports 2: 201-210 (2016).
Z.H. Hulio, W. Jiang, and S. Rehman. Technical and economic assessment of wind power potential of Nooriabad, Pakistan. Energy, sustainability and Society 7(1): 1-14 (2017).
M. Ahmed, M. N. Ali, and I. A. Memon. A review of wind energy potential in Sindh, Pakistan. Paper presented at the AIP Conference Proceedings (2019).
S. Reman, M. U. Qazi, I. Siddiqui, and N. Shah. Comparing Geostatistical and Non-geostatistical Techniques for the Estimation of Wind Potential in Un-sampled Area of Sindh, Pakistan. European Academic Research 1770-1792 (2013).
P. Gipe. Wind power, Wind Engineering, James & James/Earthscan, London 28(5): 629-631 (2004).
S. Ali, A. Mahdi, and A. H. Shaban. Wind speed estimation for Iraq using several spatial interpolation methods. environmental protection, 1: 2 (2012).
D.G. Krige. Two-dimensional weighted moving average trend surfaces for ore-evaluation. Journal of the South African Institute of Mining and Metallurgy 66: 13-38 (1966).
E. Sreevalsan. Indian Wind Atlas: Centre for Wind Energy Technology (2010).
A. Getis. A history of the concept of spatial autocorrelation: A geographer's perspective. Geographical analysis 40(3): 297-309 (2008).
