Classical Decomposition Time Series Predictive Model for the Forecast of Domestic Electric Energy Demand and Supply

Abstract

In modern technology and systems modeling, electric energy forecasting is extremely vital in gaining effective application of energy policies. This model is formulated after a thorough study of the power load conditions of Ghana as well as the factors that affect domestic electricity demand of supply in the Country was conducted. In Ghana, the LEAP (Long-range Energy Alternatives Planning) 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. Results from the quantitative classical multiplicative decomposition forecast model is comparatively precise with a reduced forecast error margin between − 5–4.5% 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.