Browsing by Author "Zaim, S."

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    ArticlePublicationOpen Access
    Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods
    (TÜBİTAK, 2012) Demirel, Ö. F.; Zaim, S.; Çalışkan, A.; Özuyar, Pınar Gökçin; Entrepreneurship; ÖZUYAR, Pinar
    The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods.
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    Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
    (Elsevier, 2019-05) Beyca, Ö. F.; Ervural, B. C.; Tatoglu, E.; Özuyar, Pınar Gökçin; Zaim, S.; Entrepreneurship; ÖZUYAR, Pinar
    Commensurate with unprecedented increases in energy demand, a well-constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.