Iranica Journal of Energy & Environment

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Editorial Staff

Best Academic Tools

Specialized Indexing Databases

Related Links

FAQ

News

Publication Ethics

Predatory and Misleading Metrics

Paper Preparation Guide

Paper Template

Journal Forms

Word Count Policy

Editors

Web of Science Profile

Reviewers

Reviewers Responsibilities

Peer Review Process

Web of Science Profile

Be as Top Peer Reviewer & Editor

On Wind Speed and its Distribution Pattern: A Case Study of Some Selected Cities in Delta State, Nigeria

Document Type : Original Article

Authors

Department of Mathematics, Faculty of Science, Edo State University Uzairue, Nigeria

Abstract

Wind is a significant weather variable and its study has gained convincing attention recently due to its increasing importance as a source of renewable energy as well as its role in various natural phenomena like erosion, precipitation, and spread of wildfires. This paper investigates wind speed distribution in Delta State, Nigeria using a nonparametric statistical technique for ten consecutive years spanning from 2011 to 2020 across three stations. The nonparametric statistical approach is the kernel density estimation with focus on Gaussian kernel estimator. The results of the investigated period revealed that wind speed in Asaba that is located in Delta North is higher in comparison with the wind speed in Patani which is situated in Southern region of the State while the wind speed is low at Sapele in Delta Central. Therefore, installation of wind power generation system is more profiting in the Northern part because the amount of wind energy generated is determine by the wind speed. Again, the performance of agricultural or industrial activities that depend on wind speed for their proper execution is optimum in 2018 while the least performances were recorded in 2015 and 2016 respectively for the period explored.

Keywords

Main Subjects

References
  1. Onoruoiza, M.R., Adedipe, O., Lawal, S.A., Olugboji, O.A., and Nwachukwu, V.C., 2022. Analysis of offshore wind energy potential for power generation in three selected locations in Nigeria. African Journal of Science, Technology, Innovation and Development, 14(3), pp.774–789. Doi: 10.1080/20421338.2021.1899760
  2. Veers, P., Dykes, K., Basu, S., Bianchini, A., Clifton, A., Green, P., Holttinen, H., Kitzing, L., Kosovic, B., Lundquist, J.K., Meyers, J., O’Malley, M., Shaw, W.J., and Straw, B., 2022. Grand Challenges: wind energy research needs for a global energy transition. Wind Energy Science, 7(6), pp.2491–2496. Doi: 10.5194/wes-7-2491-2022
  3. Ryberg, D.S., Caglayan, D.G., Schmitt, S., Linßen, J., Stolten, D., and Robinius, M., 2019. The future of European onshore wind energy potential: Detailed distribution and simulation of advanced turbine designs. Energy, 182, pp.1222–1238. Doi: 10.1016/j.energy.2019.06.052
  4. Crabtree, C.J., Zappalá, D., and Hogg, S.I., 2015. Wind energy: UK experiences and offshore operational challenges. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 229(7), pp.727–746. Doi: 10.1177/0957650915597560
  5. Bazionis, I.K., and Georgilakis, P.S., 2021. Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research. Electricity, 2(1), pp.13–47. Doi: 10.3390/electricity2010002
  6. Abdul Majid, A., 2022. Accurate and efficient forecasted wind energy using selected temporal metrological variables and wind direction. Energy Conversion and Management: X, 16, pp.100286. Doi: 10.1016/j.ecmx.2022.100286
  7. Hu, Y.-L., and Chen, L., 2018. A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm. Energy Conversion and Management, 173, pp.123–142. Doi: 10.1016/j.enconman.2018.07.070
  8. Xie, A., Yang, H., Chen, J., Sheng, L., and Zhang, Q., 2021. A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network. Atmosphere, 12(5), pp.651. Doi: 10.3390/atmos12050651
  9. Kamgba, F., Edet, C., and Njok, A., 2017. Effects of some meteorological parameters on wind energy potential in Calabar, Nigeria. Asian Journal of Physical and Chemical Sciences, 4(1), pp.1–7.
  10. Abbas, K., Alamgir, K.S.A., Ali, A., Khan, D.M., and Khalil, U., 2012. Statistical analysis of wind speed data in Pakistan. World Applied Sciences Journal, 18(11), pp.1533–1539.
  11. Attabo, A.A., Ajayi, O.O., and Oyedepo, S.O., 2019. Wind energy generation from Nigeria continental shelf: A review. IOP Conference Series: Earth and Environmental Science, 331(1), pp.012019. Doi: 10.1088/1755-1315/331/1/012019
  12. Hulio, Z.H., 2021. Assessment of Wind Characteristics and Wind Power Potential of Gharo, Pakistan. Journal of Renewable Energy, 2021, pp.1–17. Doi: 10.1155/2021/8960190
  13. Siloko, I.U., and Siloko, E.A., 2023. An Investigation on Interdependence Between Rainfall and Temperature in Ekpoma, Edo State, Nigeria. Iranica Journal of Energy and Environment, 14(3), pp.197–204. Doi: 10.5829/IJEE.2023.14.03.01
  14. Ziaei, S.M., 2022. The impacts of household social benefits, public expenditure on labour markets, and household financial assets on the renewable energy sector. Renewable Energy, 181, pp.51–58. Doi: 10.1016/j.renene.2021.09.017
  15. Raktate, T., and Choudhary, R., 2020. Design of Monopile Foundation for Offshore Wind Turbine. E3S Web of Conferences, 170, pp.01024. Doi: 10.1051/e3sconf/202017001024
  16. Saidur, R., Rahim, N.A., Islam, M.R., and Solangi, K.H., 2011. Environmental impact of wind energy. Renewable and Sustainable Energy Reviews, 15(5), pp.2423–2430. Doi: 10.1016/j.rser.2011.02.024
  17. Zhang, X., Ma, C., Song, X., Zhou, Y., and Chen, W., 2016. The impacts of wind technology advancement on future global energy. Applied Energy, 184, pp.1033–1037. Doi: 10.1016/j.apenergy.2016.04.029
  18. Adeyeye, K., Ijumba, N., and Colton, J., 2020. Exploring the environmental and economic impacts of wind energy: a cost-benefit perspective. International Journal of Sustainable Development & World Ecology, 27(8), pp.718–731. Doi: 10.1080/13504509.2020.1768171
  19. Galparsoro, I., Menchaca, I., Garmendia, J.M., Borja, Á., Maldonado, A.D., Iglesias, G., and Bald, J., 2022. Reviewing the ecological impacts of offshore wind farms. npj Ocean Sustainability, 1(1), pp.1. Doi: 10.1038/s44183-022-00003-5
  20. Bonanno, R., Viterbo, F., and Maurizio, R.G., 2023. Climate change impacts on wind power generation for the Italian peninsula. Regional Environmental Change, 23(1), pp.15. Doi: 10.1007/s10113-022-02007-w
  21. Bonanno, R., Lacavalla, M., and Sperati, S., 2019. A new high‐resolution Meteorological Reanalysis Italian Dataset: MERIDA. Quarterly Journal of the Royal Meteorological Society, 145(721), pp.1756–1779. Doi: 10.1002/qj.3530
  22. Bonanno, R., and Lacavalla, M., 2020. A feasibility analysis aimed at defining an alert system for Distribution MV Underground Cables. In: 2020 AEIT International Annual Conference (AEIT). IEEE, pp 1–6.
  23. Gonzalez, P.L.M., Brayshaw, D.J., and Zappa, G., 2019. The contribution of North Atlantic atmospheric circulation shifts to future wind speed projections for wind power over Europe. Climate Dynamics, 53(7–8), pp.4095–4113. Doi: 10.1007/s00382-019-04776-3
  24. Moemken, J., Reyers, M., Feldmann, H., and Pinto, J.G., 2018. Future Changes of Wind Speed and Wind Energy Potentials in EURO-CORDEX Ensemble Simulations. Journal of Geophysical Research: Atmospheres, 123(12), pp.6373–6389. Doi: 10.1029/2018JD028473
  25. Pryor, S.C., Barthelmie, R.J., Bukovsky, M.S., Leung, L.R., and Sakaguchi, K., 2020. Climate change impacts on wind power generation. Nature Reviews Earth & Environment, 1(12), pp.627–643. Doi: 10.1038/s43017-020-0101-7
  26. Tobin, I., Vautard, R., Balog, I., Bréon, F.-M., Jerez, S., Ruti, P.M., Thais, F., Vrac, M., and Yiou, P., 2015. Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Climatic Change, 128(1–2), pp.99–112. Doi: 10.1007/s10584-014-1291-0
  27. Hodge, B.S., Jain, H., Brancucci, C., Seo, G., Korpås, M., Kiviluoma, J., Holttinen, H., Smith, J.C., Orths, A., Estanqueiro, A., Söder, L., Flynn, D., Vrana, T.K., Kenyon, R.W., and Kroposki, B., 2020. Addressing technical challenges in 100% variable inverter‐based renewable energy power systems. Wiley Interdisciplinary Reviews: Energy and Environment, 9(5), pp.e376. Doi: 10.1002/wene.376
  28. Holttinen, H., Kiviluoma, J., Flynn, D., Smith, J.C., Orths, A., Eriksen, P.B., Cutululis, N., Soder, L., Korpas, M., Estanqueiro, A., MacDowell, J., Tuohy, A., Vrana, T.K., and O’Malley, M., 2022. System Impact Studies for Near 100% Renewable Energy Systems Dominated by Inverter Based Variable Generation. IEEE Transactions on Power Systems, 37(4), pp.3249–3258. Doi: 10.1109/TPWRS.2020.3034924
  29. Cole, W.J., Greer, D., Denholm, P., Frazier, A.W., Machen, S., Mai, T., Vincent, N., and Baldwin, S.F., 2021. Quantifying the challenge of reaching a 100% renewable energy power system for the United States. Joule, 5(7), pp.1732–1748. Doi: 10.1016/j.joule.2021.05.011
  30. Hayati, M.R., Ranjbar, S., Abdar, M.R., Molaei Nasab, M., Homayounmajd, S., and Esmaeili Shayan, M., 2023. A Comparative Analysis of Solar Energy Strategies in Middle East with Rich Fossil Resources. Iranica Journal of Energy and Environment, 14(3), pp.271–288. Doi: 10.5829/IJEE.2023.14.03.09
  31. Chen, X., Jiang, H., Cheng, H., and Zheng, H., 2023. Application of Correlation Analysis Based on Principal Components in the Study of Global Temperature Changes. Iranica Journal of Energy and Environment, 14(4), pp.336–345. Doi: 10.5829/IJEE.2023.14.04.03
  32. Tahiru, A.W., Takal, S.U., Sunkari, E.D., and Ampofo, S., 2023. A Review on Renewable Energy Scenario in Ethiopia. Iranica Journal of Energy and Environment, 14(4), pp.372–384. Doi: 10.5829/IJEE.2023.14.04.07
  33. Staffell, I., and Pfenninger, S., 2016. Using bias-corrected reanalysis to simulate current and future wind power output. Energy, 114, pp.1224–1239. Doi: 10.1016/j.energy.2016.08.068
  34. Olabi, A.G., Obaideen, K., Abdelkareem, M.A., AlMallahi, M.N., Shehata, N., Alami, A.H., Mdallal, A., Hassan, A.A.M., and Sayed, E.T., 2023. Wind Energy Contribution to the Sustainable Development Goals: Case Study on London Array. Sustainability, 15(5), pp.4641. Doi: 10.3390/su15054641
  35. Idris, W.O., Ibrahim, M.Z., and Albani, A., 2020. The Status of the Development of Wind Energy in Nigeria. Energies, 13(23), pp.6219. Doi: 10.3390/en13236219
  36. Oyewole, J.A., and Aro, T.O., 2018. Wind speed pattern in Nigeria (a case study of some coastal and inland areas). Journal of Applied Sciences and Environmental Management, 22(1), pp.119. Doi: 10.4314/jasem.v22i1.22
  37. Danlami, D., Idris, S., Thlakma, R.S., and Gwandum, G.S., 2019. The Spatio-temporal Variations of Wind Speed during Harmattan Season in Northeastern Nigeria. Geosfera Indonesia, 4(2), pp.105. Doi: 10.19184/geosi.v4i2.11474
  38. Elemo, E.O., Ogobor, E.A., Alagbe, G.A., Ayantunji, B.G., Mangete, O.E., Tomori, O.S., Doherty, K.B., and Onuh, B.O., 2021. Statistical Analysis of the Average Wind Speeds and Maximum Wind Speed (Gust Winds) at a Location in Abuja, Nigeria. OALib, 08(12), pp.1–22. Doi: 10.4236/oalib.1107935
  39. Nigerian Meteorological Agency, Federal Secretariat Complex, Okpanam Road, Asaba, Delta State, Nigeria.
  40. Silverman, B.W., 1986. Density estimation for statistics and data analysis (Vol. 26). CRC press.
  41. Siloko, I.U., Nwankwo, W., and Umezuruike, C., 2020. A discourse on smoothing parameterizations using hypothetical dataset. Journal of Applied Sciences, 1(1), pp.80–88.
  42. Wand, M. P. and Jones, M. C., 1995. Kernel Smoothing, Chapman and Hall, London.
  43. Siloko, I.U., Ukhurebor, K.E., Siloko, E.A., Enoyoze, E., Bobadoye, A.O., Ishiekwene, C.C., Uddin, O.O., and Nwankwo, W., 2021. Effects of some meteorological variables on cassava production in Edo State, Nigeria via density estimation. Scientific African, 13, pp.e00852. Doi: 10.1016/j.sciaf.2021.e00852
  44. Scott, D.W., 2015. Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.
  45. Siloko, I. U., Ishiekwene, C. C. and Oyegue, F. O., 2018. New Gradient Methods for Bandwidth Selection in Bivariate Kernel Density Estimation. Mathematics and Statistics, 6(1), pp.1–8. Doi: 10.13189/ms.2018.060101
  46. Tsuruta, Y., and Sagae, M., 2020. Theoretical properties of bandwidth selectors for kernel density estimation on the circle. Annals of the Institute of Statistical Mathematics, 72(2), pp.511–530. Doi: 10.1007/s10463-018-0701-x
  47. Siloko, I.U., and Siloko, E.A., 2019. Density estimation and data analysis using the kernel method. In: Proceedings of 56th National Conference of Mathematical Association of Nigeria. pp 218–226.
  48. Somé, S.M., and Kokonendji, C.C., 2022. Bayesian selector of adaptive bandwidth for multivariate gamma kernel estimator on [0,∞ ) d. Journal of Applied Statistics, 49(7), pp.1692–1713. Doi: 10.1080/02664763.2021.1881456
  49. Aweda, F.O., and Samson, T.K., 2022. Relationship between Air Temperature and Rainfall Variability of Selected Stations in Sub-Sahara Africa. Iranica Journal of Energy and Environment, 13(3), pp.248–257. Doi: 10.5829/IJEE.2022.13.03.05
  50. Siloko, I. U., Ukhurebor, K. E., Siloko, E. A., Enoyoze, E. and Ikpotokin. O., 2021. The Interactions between Temperature and Relative Humidity: Results for Benin City, Nigeria using Statistical Analysis. Current Applied Science and Technology, 22(1), pp. 1–15. doi: 10.55003/cast.2022.01.22.009
  51. Ukhurebor, K.E., and Siloko, I.U., 2020. Temperature and rainfall variability studies within South-South region of Nigeria. AU eJournal of Interdisciplinary Research (ISSN: 2408-1906), 5(2).
  52. Siloko, I.U., Ukhurebor, K.E., Ishiekwene, C.C., Siloko, E.A., Uddin, O.O., and Enoyoze, E., 2021. Statistical estimation of some meteorological variables using the beta kernel function. Ethiopian Journal of Environmental Studies & Management, 14(4), pp.474–486. Doi: https://doi.org///ejesm.org/doi/v14i4.7
  53. Ejemeyovwi, O.D., 2019. Geographic information system assessment of the accessibility of public and private hospitals in delta state: A study of delta central senatorial district. International Journal of Development and Sustainability, 8(12), pp.768–784.
  54. National Population Commission. 2006 Housing and population census result: Delta State National population Office, Asaba.
  55. Emaziye, P.O., 2015. The influences of temperature and rainfall on the yields of maize, yam and cassava among rural households in Delta State, Nigeria. Journal of Biology, Agriculture and Healthcare, 5(1), pp.63–69.
  56. Nkakini, S.O., and Etenero, F.O., 2019. Agricultural tractor and machinery performance and serviceability in Delta State, Nigeria. Journal of Engineering and Technology Research, 11(5), pp.47–57.
  57. Siloko, I.U., Ikpotokin, O., and Siloko, E.A., 2018. On Evaluation of Smoothing Matrix Performance in Multivariate Kernel Density Estimation. In: Proceedings of Second International Conference of Professional Statisticians Society of Nigeria. pp 268–272.
  58. Siloko, I. U. and Ojobor, S. A., 2023. A Comparative Study of Higher Order Kernel Estimation and Kernel Density Derivative Estimation of the Gaussian Kernel Estimator with Data Application. Pakistan Journal of Statistics and Operation Research, 19(2), pp.299–311. Doi: 10.18187/pjsor.v19i2.4233
Iranica Journal of Energy & Environment
Volume 15, Issue 1
January 2024
Pages 111-122
Files
Share
How to cite
Statistics