Open AccessForecasting Electricity Demand In Ghana With The Sarima Model
Author(s) -
Prince Yaw Andoh,
Charles Kofi Kafui Sekyere,
Lena Dzifa Mensah,
D E K Dzebre
Publication year - 2021
Publication title -
journal of applied engineering and technological science
Language(s) - English
Resource type - Journals
eISSN - 2715-6079
pISSN - 2715-6087
DOI - 10.37385/jaets.v3i1.288
Subject(s) - electricity , revenue , flexibility (engineering) , framing (construction) , demand forecasting , peak demand , demand response , environmental economics , electricity market , mains electricity , computer science , time series , operations research , economics , power (physics) , finance , engineering , physics , management , structural engineering , quantum mechanics , machine learning , electrical engineering
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.