STATISTICAL EVALUATION OF SURFACE WIND METHOD FOR ELECTRIFICATION IN KEBBI STATE, NIGERIA
Africa and Nigeria in particular is blessed with abundant and constant supplies of fine, clean and sustainable mean for rural and urban electricity generation (renewable energy). Renewable energy such as solar, Hydro, Geothermal, Biomass and surface wind etc., has been found very useful in power generation to many sub-Saharan African countries with attendant significant sustainability and reliability. This study was aimed at evaluating and assessing the potentiality of surface wind in Kebbi state for possible power generation thereby mitigating the challenge of energy crisis and demands for rapidly growing population. The suitable model used for the data analysis was ARIMA (1,1,2), and statistics were checked and stationarity of the data were observed and test using Kwiatkowski Phillips, Schmidt and Shin (KPSS) test. The study area from the analysis of surface wind at 0.01%, 0.05% & 0.1% level of significances depicts well for reliability and sustainability for electricity generation in the state. The study found that surface wind due to its abundance in significant amount throughout the year all over the state with highest recorded values obtained in 2009, 2010 and 2011, if well-harnesses and utilizes it could serve as a good prospect for power generation in Kebbi state and its environs.
Abdulkarim A., Abdelkader, S.M & Morrow, D. J (2017). "Statistical Analyses of Wind and Solar Energy Resources for the Development of Hybrid Microgrid," in 2nd International Congress on Energy Efficiency and Energy Related Materials (ENEFM2014). Switzerland: Springer International Publishing, pp. 9-14, 2015.
Ahmed S. S and Mohammed, H. O (2015)"A statistical analysis of wind power density based on the Weibull and Raleigh models of Penjwen region Sulaimani-Iraq," JJMIE, vol. 6, no. 2, pp. 135-140, 2012.
Ali, N. C (2003) "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, vol. 29, pp. 593–604, 2003.
Anderson O.D (1979). Time series Analysis and Forecasting: The Box-Jenkins Approach. But Tamworth, London; Boston.
Box, G. E. P. and Jenkins, G. M (1976). Time series analysis forecasting and control. Fourth Edition (Willey series incorporated).
Dickey D. A. and W. A. Fuller, (1979). Distributions of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc., 74: 427-431.
Emmanuel. P. Agbo, Collins. O. Edet, Thomas O. Magu, Armstrong. O. Njok, Chris.M. Ekpo, Hitler Louis, (2021). Solar energy: A panacea for the electricity generation crisis in Nigeria: Heliyon 7 (2021) e07016. www.cell.com/heliyon.
Kwiatkowski, D., Peter, C. B., Schmidt, P. and Shin.Y.(1992). Testing the null hypothesis of stationarity against the alternative of the unit root. Journal of econometrics, 54,159-176
Ljung, G. M. and Box, G. E. P. (1978). “On a Measure of a Lack of Fit in Time Series Models,” Biometrika, 65, 297-303.
Newbold, P and Gringer, P. (1974). The exact likelihood function for a mixed auto-regressive moving average process, Biometrika, 71, 197-202.
Ozoegwu, C. G, Mgbemene, C. A & Ozor, P. A. (2017). The status of solar energy integration and policy in Nigeria, Renew. Sustain. Energy Rev. 70, Pp: 457–471.
Phadke, M. S., Kedem, G. (1978). Computation of exact likelihood function of multivariate moving average models, Biometrika, 65, 511-519
Uyigue, E. M. Agbo, A. Edeubara (2007). Promoting renewable energy and efficiency in Nigeria, in: The Report of a One-Day Conference Which Held at the University of Calabar.
Zaharim, A., Razali A. M, Abidin, R. Z and Sopian, K (2009) "Fitting of statistical distributions to wind speed data in Malaysia," European Journal of Scientific Research, vol. 26, no. 1, 2009.
Copyright (c) 2021 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences