Adequate power supply is a vital factor in the development of the
economic growth of every nation. However, due to changing hydrological
conditions, inadequate fuel supplies and dilapidated infrastructure, developing
countries face challenges in planning the power grid infrastructure needed to
support rapidly growing urban populations. This research seeks to model the
monthly electricity power generation for
prediction purposes, by implementing stochastic process models on a
historical series of monthly electricity power generation in Ghana. A detailed explanation of model selection and
forecasting accuracy is presented. The SARIMA (1,0,0)×(0,1,1)12 model with an AIC
score of 439.6995, a BIC score of 446.3537 and an AICc score of 440.8759, has
been identified as an appropriate model for predicting monthly electricity
power generation in Ghana. The range used was from 2015 to 2019 and it was
validated with data from April to December of 2019. The predicted values for
2019 are relatively close to the observed values. Thus, the experimental
results
References
[1]
Adusei, L. (2012) Energy Security and the Future of Ghana.
http://newsghana.com.gh/energy-security-and-the-future-of-ghana/
[2]
Ghana Growth and Development Platform (2015) Of “Dumsor” and Ghana’s Energy Sector Challenges: Part 3. http://ghanagdp.org/
[3]
Energy Commission of Ghana (2016) 2016 Energy (Supply and Demand) Outlook for Ghana—FINAL.
http://www.energycom.gov.gh/files/Energy%20Commission%20-%202016Energy%20Outlook%20for%20Ghana_final.pdf
[4]
Ghana Energy Commission (2018) Ghana Wholesale Electricity Market Bulletin— December 2018. Technical Report 36.
[5]
United Nations Statistical Commission (2017) Revised List of Global Sustainable Development Goal Indicators. Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators (E/CN. 3/2017/2).
[6]
Owusu, P. and Asumadu-Sarkodie, S. (2016) A Review of Renewable Energy Sources, Sustainability Issues and Climate Change Mitigation. Cogent Engineering, 3, Article ID: 1167990. https://doi.org/10.1080/23311916.2016.1167990
[7]
DeVynne, F., Jaramillo, P. and Samaras, C. (2018) Sustainability Implications of Electricity Outages in Sub-Saharan Africa. Nature Sustainability, 1, 589-597.
https://doi.org/10.1038/s41893-018-0151-8
[8]
United Nations in Ghana (n.d.) 2030 Agenda for Sustainable Development.
http://gh.one.un.org/content/unct/ghana/en/home/global-agenda-in-ghana/sustainable-developmentgoals.html
[9]
Awoponea, A.K., Zobaa, A.F. and Banuenumah, W. (2017) Assessment of Optimal Pathways for Power Generation System in Ghana. Cogent Engineering, 4, Article ID: 1314065. https://doi.org/10.1080/23311916.2017.1314065
[10]
Katara, S., Faisal, A. and Engman, G.M. (2014) A Time Series Analysis of Electricity Demands in Tamale, Ghana. International Journal of Statistics and Applications, 4, 267-275.
[11]
Twumasi-Ankrah, S. and Ankrah, I. (2015) Prediction of Electricity Consumption in Ghana: Long or Short Memory. Advances in Economics and Business, 3, 107-117.
https://doi.org/10.13189/aeb.2015.030304
[12]
World Bank (2020) Consumption of Electrical Energy (kWh per capita).
https://www.worlddata.info/africa/ghana/energy-consumption.php
[13]
Shumway R. H and Stoffer D. S. (2011) Time Series Analysis and Its Applications with R Examples. 3rd Edition, Springer, New York.
[14]
McCleary, R., Hay, R.A., Meidinger, E.E. and McDowall, D. (1980) Applied Time Series Analysis for the Social Sciences. Sage Publications, Beverly Hills.
[15]
Box, G.E.P. and Pierce, D.A. (1970) Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models. Journal of the American Statistical Association, 65, 1509-1526.
https://doi.org/10.1080/01621459.1970.10481180
[16]
Chan, K. (2008) Time Series Analysis with Applications in R. Springer Science, New York.
[17]
Canova, F. and Hansen, B.E. (1995) Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability. Journal of Business & Economic Statistics, 13, 237-252.
https://doi.org/10.1080/07350015.1995.10524598
[18]
Sugiura, N. (1978) Further Analysts of the Data by Akaike’s Information Criterion and the Finite Corrections. Communications in Statistics—Theory and Methods, 7, 13-26. https://doi.org/10.1080/03610927808827599
[19]
Diebolt, F.X. (1998) Elements of Forecasting. South-Western College Publishing, Cincinnati.
[20]
Abraham, B.J. and Ledolter, R.A. (2000) Introduction to Time Series and Forecasting. John Wiley and Sons, New York.
[21]
Ljung, G. and Box, G. (1978) On a Measure of a Lack of t in Time Series Models. Biometrika, 65, 297-303. https://doi.org/10.1093/biomet/65.2.297
[22]
Wiah, E.N. and Twumasi-Ankrah, S. (2017) Impact of Climate Change on Cocoa Yield in Ghana Using Vector Autoregressive Model. Ghana Journal of Technology, 1, 32-39.
