Forecasting Electricity Demand In Ghana With The Sarima Model


  • Prince Yaw Andoh Andoh Kwame Nkrumah University of Science and Technology
  • Charles Kofi Kafui Sekyere Kwame Nkrumah University of Science and Technology
  • L. D. Mensah Kwame Nkrumah University of Science and Technology
  • D. E.K. Dzebre Kwame Nkrumah University of Science and Technology



Electricity demand, Forecasting SARIMA, Ghana


Demand forecasting is a challenging subject of interest to many organizations whose main focus is to improve their steady growing customer request/demand, and help in increasing their revenue generation. The story is no different in the power industry. It is quite difficult for power or electrical producers to store high quantum of the energy produced, hence this poses a challenge in estimating precisely the quantum of electrical energy in order to equate demand and supply of powers as well as reducing or eliminating the rising transmission losses. This study explores potential time series models in electricity demand prediction or forecasting for the Western Regions of Ghana. Secondary data was sourced formally from the regional headquarters of ECG to aid in research design to be able to estimate the quantum of electricity needed by consumers in the region. This was done using time series data analysis toolpak software. Results show that the models formulated are viable for future consumption forecasts and other investment in alternative power source projects in meeting these future demands. Since there are up-surging energy demand patterns in the region, the flexibility of the formulated models can be very useful and supplementary to framing effective and efficient energy policies.


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Abdel-Aal, R. E., & Al-Garni, A. Z. (1997). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy, 22(11), 1059-1069.

Adom, P. K., & Bekoe, W. (2012). Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: a comparison of ARDL and PAM. Energy, 44(1), 367-380.

Ahmed, N. B. (2018). A Comparative Analysis of Forecast Performance between Sarima and Setar Models Using Macroeconomic Variables in Ghana (Doctoral dissertation, University of Ghana).

Ang, B. W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy policy, 33(7), 867-871.

Asumadu-Sarkodie, S., & Owusu, P. A. (2016). The potential and economic viability of wind farms in Ghana. Energy sources, Part A: Recovery, utilization, and environmental effects, 38(5), 695-701.

Asumadu-Sarkodie, S., & Owusu, P. A. (2016). Forecasting Nigeria’s energy use by 2030, an econometric approach. Energy Sources, Part B: Economics, Planning, and Policy, 11(10), 990-997.

Aslanargun, A., Mammadov, M., Yazici, B., & Yolacan, S. (2007). Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting. Journal of Statistical Computation and Simulation, 77(1), 29-53.

Billah, B., Hyndman, R. J., & Koehler, A. B. (2005). Empirical information criteria for time series forecasting model selection. Journal of Statistical Computation and Simulation, 75(10), 831-840.

Boisseleau, F. (2004). The role of power exchanges for the creation of a single European electricity market: market design and market regulation.

Bosq, D. (2015). Models Associated with Extended Exponential Smoothing. Communications in Statistics-Theory and Methods, 44(3), 468-475.

Box G.E.P. and Jenkins G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day, revised edition.

Box, G. E., Pierce, D. A., & Newbold, P. (1987). Estimating trend and growth rates in seasonal time series. Journal of the American Statistical Association, 82(397), 276-282.

Cheng, C., Sa-Ngasoongsong, A., Beyca, O., Le, T., Yang, H., Kong, Z., & Bukkapatnam, S. T. (2015). Time series forecasting for nonlinear and non-stationary processes: a review and comparative study. Iie Transactions, 47(10), 1053-1071.

Energy Commission of Ghana (2020), 2020 Energy Outlook for Ghana, pp 33.

Energy Commission of Ghana (2021), National Energy Statistics – 2021, pp 47.

Feinberg, E. A., & Genethliou, D. (2005). Load forecasting. In Applied mathematics for restructured electric power systems (pp. 269-285). Springer, Boston, MA.

Ghana Statistical Service, Population by Regions [accessed 2021 20 Oct]; Available from:

Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009). Electric load forecasting methods: Tools for decision making. European journal of operational research, 199(3), 902-907.

Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter.

Jain, A., Srinivas, E., & Rauta, R. (2009, December). Short term load forecasting using fuzzy adaptive inference and similarity. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 1743-1748). IEEE.

Jiang, S., Yang, C., Guo, J., & Ding, Z. (2018). ARIMA forecasting of China’s coal consumption, price and investment by 2030. Energy Sources, Part B: Economics, Planning, and Policy, 13(3), 190-195.

Kim, J., Hwang, M., Jeong, D. H., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems with Applications, 39(16), 12618-12625.

Kyriakides, E., & Polycarpou, M. (2007). Short term electric load forecasting: A tutorial. Trends in Neural Computation, 391-418.

Louie, H. M. (2017). Time-series modeling of aggregated electric vehicle charging station load. Electric Power Components and Systems, 45(14), 1498-1511.

Marcellino, M. (2007). A comparison of time series models for forecasting GDP growth and inflation. Bocconi University, Italia.

Meese, R., & Geweke, J. (1984). A comparison of autoregressive univariate forecasting procedures for macroeconomic time series. Journal of Business & Economic Statistics, 2(3), 191-200.

Sarkodie, S. A. (2017). Estimating Ghana’s electricity consumption by 2030: An ARIMA forecast. Energy Sources, Part B: Economics, Planning, and Policy, 12(10), 936-944.

Shi, J., Qu, X., & Zeng, S. (2011). Short-term wind power generation forecasting: Direct versus indirect ARIMA-based approaches. International Journal of Green Energy, 8(1), 100-112.

Shitan, M., & Peiris, S. (2011). Time series Properties of the class of generalized first-order autoregressive processes with moving average errors. Communications in Statistics-Theory and Methods, 40(13), 2259-2275.

Stock, J. H., & Watson, M. W. (1996). Evidence on structural instability in macroeconomic time series relations. Journal of Business & Economic Statistics, 14(1), 11-30.

Stock, J. H. and Watson, M. W. (2003). Forecasting output and inflation: The role of asset prices. Journal of Economic Literature, 41(3):788–829.

Sulugodu, B., & Deka, P. C. (2019). Evaluating the performance of CHIRPS satellite rainfall data for streamflow forecasting. Water Resources Management, 33(11), 3913-3927.

Taylor, J. W., &McSharry, P. E. (2007). Short-term load forecasting methods: An evaluation based on european data. IEEE Transactions on Power Systems, 22(4), 2213-2219.

Trindade, A. A. (2002). Time-Series Forecasting.(Book Reviews). Journal of the American Statistical Association, 97(459), 920-921.

Weron, R., &Misiorek, A. (2005, May). Forecasting spot electricity prices with time series models. In Proceedings of the European electricity market EEM-05 conference (pp. 133-141).




How to Cite

Andoh, P. Y. A., Sekyere, . C. K. K., Mensah, L. D., & Dzebre, D. E. (2021). Forecasting Electricity Demand In Ghana With The Sarima Model. Journal of Applied Engineering and Technological Science (JAETS), 3(1), 1–9.