Forecasting Hydropower Generation in Ghana Using ARIMA Models

Abstract

In this study, an Autoregressive Integrated Moving Average (ARIMA) model was used to forecast Ghana’s Akosombo dam level and hydropower generation by the end of year 2022. Data used for this study span from January 2010 to December 2019. Base on the final ARIMA model, power generation is forecasted to decrease from 398 Megawatts/hr in December 2019 to approximately 374 Megawatts/hr by December 2022. On the other hand, water level of the Akosombo dam is predicted to decrease marginally from 264.8 ft in December 2019 to approximately 255.19 ft by December 2022. The Volta River Authority (VRA) and managers of the electricity production in Ghana are encouraged to be proactive in expanding energy production by turning more to renewable energy sources. In the coming years, as they seek to provide sustainable electricity for their cherished customers, investment decisions should be directed towards protecting the volta river from drying up due to human and climatic activities as well as expanding energy mix.

Supply and demand prediction by 3PL for assortment planning

“…,where: N -number of observations,; K -number of parameters; 𝐿(Ɵ) -likelihood function for a significant model Source: elaborated based on: Burnham and Anderson (2021); Sarpong (2013) The larger the k, the smoother the time series becomes, meaning outlier observations will have a diminishing influence on the model, but concurrently, as k increases, more information is lost. Models based on the moving average, similar to exponential smoothing models, are categorized as adaptive models (Carbone, 2009;Peels et al, 2009).…”