Forecasting residential natural gas consumption in Egypt

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>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.</jats:p> </jats:sec>

Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models

“…) model that incorporates seasonal patterns in the data [35,37]. Seasonality is the tendency of a time series to repeat itself over regular intervals of time, such as daily, monthly, quarterly, or yearly.…”

“…MAPE is determined by taking the average of the absolute percentage errors between the actual and forecasted values. MAPE is expressed mathematically as shown in Equation ( 8) [36,37].…”