TITLE:
Statistical Model for the Forecast of Electricity Power Generation in Ghana
AUTHORS:
Eric Neebo Wiah, Albert Buabeng, Kofi Agyarko
KEYWORDS:
Electricity, Seasonality, Power, SARIMA, Forecast
JOURNAL NAME:
Open Journal of Statistics,
Vol.12 No.3,
June
24,
2022
ABSTRACT: 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.