A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models' prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model's performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG. [ FROM AUTHOR] Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

ROMANIAN COAL BETWEEN DECARBONIZATION PROGRAMS, EXPLOITATION CLOSURE AND ENERGY CRISIS

The energy crisis triggered globally in the last quarter of 2021, forced Romania to go through a winter with liberalized electricity and gas prices, a period unprecedented in terms of consumer prices, accentuated by the result of closing energy targets that coal-fired operations as a result of the imposition of EU conditions for the restructuring of the mining and energy sector. The year before, 2020, the COVID-19 pandemic, with restrictions imposed, produced a decrease in electricity demand, a decline in coal-fired power consumption, greatly reduced the share of gas in the energy mix, using preferential renewable energy sources. Subsequently, Russian gas prices have risen to record highs and, as a result, coal-fired power generation has returned to the European Union in 2021, although coal became more expensive and emission allowance prices doubled. In the context of the armed conflict between Russia and Ukraine, major price increases are expected worldwide, generating new risks of exposing the economy and new challenges in terms of ensuring Romania's independence and energy security. Against this background of these situations, can Romania still respect the European commitments of the Green Pact, meet these challenges and respond to the needs of the communities affected by the projected programs? The present paper aims at a critical assessment of the current situation of the Romanian coal mining and future trends of using a "clean coal” as a variant to respond to the requirements of the environmental concerns. © 2022 International Multidisciplinary Scientific Geoconference. All rights reserved.

Saving natural gas through smart water heating

Water heating in Pakistan and its neighboring countries predominantly relies on inefficient, natural gas-wasting water heaters whose mechanical design has remained largely unmodified since their inception in the 1960s. The inefficiency of these water heaters has added much to the woes of depleting gas reserves of the region, leading to a widening demand-supply gap. Pakistan is facing its worst ever natural gas crisis due to a COVID-19 hit economy that cannot carry the burden of expensive imports, especially during the Russo-Ukrainian conflict that has sent the gas prices soaring in the international market. We respond to this challenge with a sense of urgency by proposing a solution that minimizes the wastage of natural gas in water heating, which consumes about half the gas supplied to residential consumers in the country. Our solution replaces the mechanical control of the water heater with an IoT-inspired, electrical retrofit design combining hardware and software for smart control through user-defined schedules or machine learning, while solving several challenges that arise from replacing a mechanical control system with an electrical one. Empirical results demonstrate 70% reduction in consumption. © 2022 ACM.

Data Mining Tools for Analysis of Dependence of Gas Consuption of the Gas Price for Housholds of the EU Memberd-States

The current crisis in the European natural gas industry, caused by the effects of the COVID-19 pandemic, extreme weather conditions, recent frequent accidents at gas plants around the world and speculation in the emissions market has actualized the problem of turning data into useful information and knowledge that can support decision-making. In this article, we used associative rules to identify non-obvious associative links in terms of pre-crisis empirical data (January 2013 - June 2021) between gas consumption and gas prices for EU Members. The results obtained can be useful in shaping an effective pricing policy in the European Union gas market and in regulating of household gas consumption © 2022 IEEE.