Author(s)

Eric Neebo Wiah, Albert Buabeng, Kofi Agyarko

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana.

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 show good prediction performances. Therefore, with developed SARIMA model, the forecast is made for the year 2021, proving an increase of monthly power generation. The performance and validation of the SARIMA model were evaluated based on various statistical measures, the test data produced RMSE (55.8606), MAE (45.454) and MAPE (3.0621%). The lagged effect can also help in accurate forecasting and assist policy and decision-makers to establish strategies, priorities on electric power generation.

Electricity, Seasonality, Power, SARIMA, Forecast

Wiah, E. , Buabeng, A. and Agyarko, K. (2022) Statistical Model for the Forecast of Electricity Power Generation in Ghana. Open Journal of Statistics, 12, 373-384. doi: 10.4236/ojs.2022.123024.