Abstract
Purpose This paper aims to obtain accurate forecasts of the hourly residential natural gas consumption, in Egypt, taken into consideration the volatile multiple seasonal nature of the gas series. This matter helps in both minimizing the cost of energy and maintaining the reliability of the Egyptian power system as well. Design/methodology/approach Double seasonal autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity model is used to obtain accurate forecasts of the hourly Egyptian gas consumption series. This model captures both daily and weekly seasonal patterns apparent in the series as well as the volatility of the series. Findings Using the mean absolute percentage error to check the forecasting accuracy of the model, it is proved that the produced outcomes are accurate. Therefore, the proposed model could be recommended for forecasting the Egyptian natural gas consumption. Originality/value The contribution of this research lies in the ingenuity of using time series models that accommodate both daily and weekly seasonal patterns, which have not been taken into consideration before, in addition to the series volatility to forecast hourly consumption of natural gas in Egypt.
Highlights
Natural gas constitutes one of the keystones of both economic and social development, as it plays an essential role in reducing pollution and maintaining environmental cleanness
Literature review autoregressive integrated moving average (ARIMA) models are extensively used in forecasting based on previously observed values of a time series
Akkurt et al (2010) used the Seasonal ARIMA (SARIMA) model, which is an extension of ARIMA model, in a comparative study with the linear regression model to forecast Turkish natural gas consumption in a monthly and yearly base through a timeseries data having single seasonal pattern
Summary
Natural gas constitutes one of the keystones of both economic and social development, as it plays an essential role in reducing pollution and maintaining environmental cleanness. Literature review ARIMA models are extensively used in forecasting based on previously observed values of a time series It has been used, in many preceding studies, to obtain accurate forecasting of natural gas consumption. Akkurt et al (2010) used the Seasonal ARIMA (SARIMA) model, which is an extension of ARIMA model, in a comparative study with the linear regression model to forecast Turkish natural gas consumption in a monthly and yearly base through a timeseries data having single seasonal pattern. Different statistical methods among ARIMA models are used, in two studies, to forecast natural gas consumption in Turkey using residential and commercial data. DSARIMA, as an extension of ARIMA model, is used in this study to accommodate daily and weekly seasonal patterns and combined with GARCH model to capture the volatility of the series to obtain accurate forecasts of the Egyptian residential gas consumption. It can be shown that working days show similar patterns of consumption, while weekends have different natural gas
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