Application of Classical Multiplicative Decomposition Time Series Predictive Model for the Forecast of Domestic Electricity Demand and Supply: A Ghanaian Context

Abstract

In modern technology and systems modeling, electric energy forecast is extremely vital for the attainment of effective application of energy policies. This model is formulated after a thorough study of the power load conditions of Ghana and the factors that affect domestic electricity demand and supply in the country was conducted. In Ghana, the Long-range Energy Alternatives Planning (LEAP) forecast model is officially applied for electricity demand and projection of power supply which comes with forecasting errors. Thus, there exists a crucial need to develop a forecasting model for the best energy policies formulation and consequent minimization of overall forecasting error compared to the LEAP model. A step-by-step mathematical approach of forecasting time series data of all the domestic electricity demand areas of Accra, namely: Mallam, Achimota and Accra East 9-year data was applied in the forecasting process. However, data for Accra east was only for four years due to the fact that it was a new distribution station at the time. Results from the quantitative classical multiplicative decomposition forecast model is comparatively precise with a reduced forecast error margin between - 5% to 4.5% compared to an existing prediction error margin viz., 1% to -11%. By virtue of the proposed study, accurate forecasting of power loads, improvement in utilization of electrical equipment, economies of scale and reduction in production cost can be attained. It is also essential to optimize power system resources for the attainment of energy conservation and overall reduction in emissions. Copyright: � 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).