Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Gaweł, Bartłomiej; Paliński, Andrzej

doi:10.3390/en14164905

Cited by 11 publications

(5 citation statements)

References 60 publications

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“…Traditional time series models are often replaced by artificial neural networks, including deep neural networks and long short-term memory (LSTM) [29][30][31]. Long-term forecasting is the research area in only about 20% of studies [10,16,17,32]. Usually, long-term forecasts are constructed using models with dependent macroeconomic variables (GDP, population, unemployment rate, etc.…”

Section: Natural Gas Forecasting Researchmentioning

confidence: 99%

Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series

Gaweł,

Paliński

2024

Energies

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This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country’s administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.

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Section: Natural Gas Forecasting Researchmentioning

confidence: 99%

Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series

Gaweł,

Paliński

2024

Energies

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“…These disruptions have worsened the difficulty of alternative last-mile options in urban logistics, resulting in novel package delivery systems that mix physical and digital solutions such as self-collection delivery systems. Self-collection delivery systems benefit both courier companies and clients by providing time-window flexibility while lowering overall mileage, delivery time, and, as a result, gas emissions [94,95].…”

Section: Graph Representations Of Automated Smart Locker System Optim...mentioning

confidence: 99%

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Sawik

2024

Logistics

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Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

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“…Using cluster mathematical analysis (i.e., analogue methods and fuzzy decision trees), ref. [16] drew attention to an innovative approach to long-term forecasting of natural gas consumption at the level of 79 countries in the period 1960-2019. Based on the macroeconomic variables used (GDP, GDP per capita, population, value added of industry to total GDP, CO 2 emissions, energy intensity of GDP, share of natural gas consumption in the energy mix, HDD index, and annual natural gas consumption), it was found that the model leads to better results, in terms of increased performance and level of accuracy, in the analysis of forecasting annual natural gas consumption and production per capita.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries

Vlăduţ,

Grigore,

Bodislav

et al. 2024

Energies

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The objective of this research paper is to apply a mathematical model to estimate and predict the economic growth of the Caspian countries in the period from 1995 to 2022. We use multiple regression by applying the OLS method to estimate the impact of global oil price, energy resource production per capita, trade, and renewable energy on GDP per capita. The mathematical approach uses fixed and random effects models to assess the overall impact of the independent variables on economic growth in this region and over the period analysed. This study also aims to investigate whether the explanatory variables are cointegrated in the long run; as such, we carry out several mathematical cointegration tests, namely the Pedroni and Johansen tests. The mathematical analysis is completed by the estimation of short- and long-run parameters using the stochastic VAR/VEC models, the impulse response function, and the causality test to assess economic growth in this region. This study’s main finding is that GDP per capita is increasingly influenced by its previous values, which is confirmed by considering lag 1 and lag 2. The results of the Granger causality tests identify several bidirectional relationships between GDP per capita and oil and gas production. These relationships are clearly positive evidence of the growth trend and progress of economic activity in the Caspian region. The practical implications of the study aim to promote and support the use of renewable energy sources. In this sense, policymakers in the Caspian countries should create favourable conditions for the transition to a green economy. An important aspect is the efforts of the government authorities to make their policies more environmentally friendly, as decarbonisation is a good practice in the current context of sustainability and related choices. As the Caspian countries are heavily dependent on conventional energy production, it is essential for them to increase their export earnings from energy resources via diversifying and strengthening new energy opportunities and partnerships.

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Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Cited by 11 publications

References 60 publications

Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series

Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries

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