Azerbaijan Government
Forecasting Monthly Residential Natural Gas Demand Using Just-In-Time-Learning Modeling
Alakent, Burak, Isikli, Erkan, Kadaifci, Cigdem, Taspinar, Tonguc S.
ABSTRACT Natural gas (NG) is relatively a clean source of energy, particularly compared to fossil fuels, and worldwide consumption of NG has been increasing almost linearly in the last two decades. A similar trend can also be seen in Turkey, while another similarity is the high dependence on impor ts for the continuous NG supply. It is crucial to accurately forecast future NG demand (NGD) in Turkey, especially, for import contracts; in this respect, forecasts of monthly NGD for the following year are of utmost importance. In the current study, the h istorical monthly NG consumption data between 2014 and 2024 provided by SOCAR, the local residential NG distribution company for two cities in Turkey, Bursa and Kayseri, was used to determine out - of - sample monthly NGD forecasts for a period of one year and nine months using various time series models, including SARIMA and ETS models, and a novel proposed machine learning method. The proposed method, named Just - in - Time - Learning - Gaussia n Process Regression (JITL - GPR), uses a novel feature representation for t he past NG demand values; instead of using past demand values as column - wise separate features, they are placed on a two - dimensional (2 - D) grid of year - month values. For each test point, a kernel function, tailored for the NGD predictions, is used in GPR t o predict the query point. Since a model is constructed separately for each test point, the proposed method is, indeed, an example of JITL. The JITL - GPR method is easy to use and optimize, and offers a reduction in forecast errors compared to traditional t ime series methods and a state - of - the - art combinat ion model; therefore, it is a promising tool for NGD forecasting in similar settings. INTRODUCTION In the last few decades, there has been a shift in energy sources from fossil fuels to cleaner energy sources, such as wind and solar energy, mainly due to environmental concerns and related government regulations . However, these latter sources are depend ent on w eather conditions and require integration with grid technologies for continuous power generation. Natural gas (NG), typically, consists of (up to) ~95% of methane and 2 - 2.5% ethane - hexane+, with the remain der consist ing of nitrogen, CO NG p ower plants are easy to build and highly reliable, mak ing them invaluable for "clean" energy production. On the other hand, m ost countries depend on imports to maintain t heir NG supplies, and there is a delicate balance between import s and domestic demand . S toring excess import ed gas above actual demand is difficult and would result in economic losses, while import ing less than actual demand could result in a nationwide sh ortage.
- North America > United States (0.67)
- Asia > Azerbaijan (0.49)
- Asia > Middle East > Republic of Türkiye > Kayseri Province > Kayseri (0.26)
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- Energy > Oil & Gas > Midstream (1.00)
- Energy > Oil & Gas > Downstream (0.84)
- Energy > Renewable > Solar (0.74)
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- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.94)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (0.67)
Putin apologises to Azerbaijan's president over 'tragic' plane crash
Russian President Vladimir Putin has apologised to his Azerbaijani counterpart Ilham Aliyev for what he called a "tragic incident" following the deadly crash of an Azerbaijan Airlines plane this week in Kazakhstan. The plane was flying on Wednesday from Azerbaijan's capital of Baku to Grozny, the regional capital of the Russian republic of Chechnya, when it turned towards Kazakhstan and crashed while attempting to land. In a statement on Saturday, the Kremlin said Russian air defence systems were firing near Grozny due to a Ukrainian drone strike, but stopped short of saying one of these hit the plane. "Vladimir Putin apologised for the tragic incident that occurred in Russian airspace and once again expressed his deep and sincere condolences to the families of the victims and wished a speedy recovery to the injured," the Kremlin said. "At that time, Grozny, Mozdok and Vladikavkaz were being attacked by Ukrainian unmanned aerial vehicles, and Russian air defence systems repelled these attacks."
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- Asia > Azerbaijan (1.00)
- Europe > Russia > North Caucasian Federal District > Chechen Republic > Grozny (0.69)
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- Government > Regional Government > Europe Government > Russia Government (1.00)
- Government > Regional Government > Asia Government > Russia Government (1.00)
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