Abstract
The increasing shortage of electricity in Pakistan disturbs almost all sectors of its economy. As, for accurate policy formulation, precise and efficient forecasts of electricity consumption are vital, this paper implements a forecasting procedure based on components estimation technique to forecast medium-term electricity consumption. To this end, the electricity consumption series is divided into two major components: deterministic and stochastic. For the estimation of deterministic component, we use parametric and nonparametric models. The stochastic component is modeled by using four different univariate time series models including parametric AutoRegressive (AR), nonparametric AutoRegressive (NPAR), Smooth Transition AutoRegressive (STAR), and Autoregressive Moving Average (ARMA) models. The proposed methodology was applied to Pakistan electricity consumption data ranging from January 1990 to December 2015. To assess one month ahead post-sample forecasting accuracy, three standard error measures, namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were calculated. The results show that the proposed component-based estimation procedure is very effective at predicting electricity consumption. Moreover, ARMA models outperform the other models, while NPAR model is competitive. Finally, our forecasting results are comparatively batter then those cited in other works.
Highlights
Electricity is a key component for the growth and development of any country’s economy
It is worth mentioning that our best Mean Absolute Percentage Error (MAPE) values are comparatively batter than those cited in other works
To check the forecasting performance of all models, consumption data from Pakistan were used, and one month ahead post-sample forecasts were obtained for four years
Summary
Electricity is a key component for the growth and development of any country’s economy. Many researchers have worked on medium-term electricity demand forecasting that generally ranges from one month to a few months ahead using different methods, including time series, regression, artificial intelligent, genetic algorithm, fuzzy logic, and support vector machine [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]. To account for these effects, Nawaz et al [21] studied Pakistan’s annual electricity consumption with the help of economic variables They forecasted electricity demand up to 10 years ahead using Smooth Transition Auto-Regressive (STAR) model. The purpose of this study was to develop and evaluate model(s) for forecasting medium-term electricity consumption time series.
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