Forecasting and Assessment of the Energy Security Risk in Fuzzy Environment

Ziemba, Paweł; Becker, Aneta; Becker, Jarosław

doi:10.3390/en14185934

Cited by 10 publications

(3 citation statements)

References 62 publications

(78 reference statements)

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“…The values for 2018 were used as present data, i.e., the latest data published in the report. On the other hand, the forecasted index values were determined using the Holt model borrowed from the work of Ziemba et al [50]. These forecasts were prepared for 2025 using the 1980-2015 time series (for Russia and Ukraine it was the 1995-2015 time series due to the lack of previous data) with a step of 5 years.…”

Section: Resultsmentioning

confidence: 99%

Energy Security Assessment Based on a New Dynamic Multi-Criteria Decision-Making Framework

Ziemba

2022

Energies

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Access to energy resources and broadly understood energy security are some of the critical factors influencing the economic development of countries. This article deals with the problem of assessing the energy security of countries, considering this problem in various periods of time, examining the past, present and forecasted future conditions at the same time. For this purpose, the Dynamic Multi-Criteria Decision Making (DMCDM) methodology was developed and applied, based on the classic and fuzzy Multi-Criteria Decision Making (MCDM) methods and the International Energy Security Risk Index (IESRI). In particular, the Simple Additive Weighting (SAW)/Fuzzy SAW and New Easy Approach to Fuzzy PROMETHEE II (NEAT F-PROMETHEE) methods were used. These methods are significantly different from each other in the calculation procedures used. The study showed that methodological differences between these methods cause large differences in the results of the assessment of energy security of countries. However, both methodological approaches indicated the high energy security of New Zealand, Norway, Denmark and the United States, and the very low security of Ukraine, Thailand and South Korea. The results of the assessment of energy security of countries over the 2015–2025 period are the main practical contribution of this article. The scientific contribution of the article consists in developing a framework for dynamic energy security assessment that allows for the aggregation of many periods of time and that defines the aggregation strategies, capturing data from the past, present and future state forecasts while taking into account changes in the weights of criteria and changes in the sets of alternatives and criteria.

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Section: Resultsmentioning

confidence: 99%

Energy Security Assessment Based on a New Dynamic Multi-Criteria Decision-Making Framework

Ziemba

2022

Energies

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“…By integrating a complex safety index with fuzzy multi-standards decision analysis (MCDA) [18], Ziemba et al provide a novel method for assessing the current and future strength protection. Results from Holt's method and the International Energy Security Risk Index (IESRI) are utilized to generate projections.…”

Section: Literature Surveymentioning

confidence: 99%

Managing Economic Uncertainty: Fuzzy Computational Models in International Oil Economy Forecasting

Zhang

2024

Preprint

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The use of sophisticated computational models for economic forecasting and decision-making is on the rise. Several studies have compared the Hybridization Adaptive Fuzzy Inference System (HAFIS) which is proposed in this research to traditional approaches; this review looks at them all to show how HAFIS is better in several areas, including precision, flexibility, responsiveness, decision support, and long-term planning. The version's accuracy, responsiveness, flexibility, decision support, and strategic making plans talents are more suitable as the included system evolves in phases. The thorough exam of Economic Uncertainty, which is divided into 3 principal impacts: Geopolitical Events, Market Pressures, and Environmental Factors, is the critical process of HAFIS. All of these items integrate to form the unpredictable surroundings that the oil commercial enterprise works in. Economic facts is notoriously misguided, however that is all treated by means of a mixture of rule bases, fuzzy common sense operations. The complicated Forecasting Model, which includes modern Fuzzy Computational Models, is on the middle of this level and can react dynamically to the various troubles posed by means of economic unpredictability and global marketplace tendencies. The fashions use adaptive procedures and fuzzy logic to decipher complex patterns inside the oil enterprise's complex fabric. The endorsed HAFIS method is portrayed as a complete and flexible technique to the challenges of working inside the unpredictable worldwide oil market. The use of actual-world data within the simulation evaluation proved that HAFIS outperformed extra traditional techniques of predicting. Because of its flexibility and flexibility, HAFIS has the potential to generate accurate projections, making it a doubtlessly beneficial asset for everyone involved inside the oil enterprise. In the end, these studies will be of assistance to professionals working in the oil industry in navigating the complexities of the global oil economic system. This will be accomplished via the development of forecasting methodologies and the demonstration of how to realistically apply such models to actual global situations.

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“…It should be noted that the way the data are represented and the type of fuzzy set used have a large impact on the complexity of the fuzzy methods. Generally, it can be said that normal fuzzy sets (type-1) in the form of TrFNs or TFNs are much easier to use and interpret [49], and also much more explainable than other, more advanced, fuzzy set implementations [32,50]. It is believed that the trapezoidal approximation is a reasonable compromise between the tendency to lose too much information and the tendency to introduce too complicated forms of approximation [51].…”

Section: Literature Reviewmentioning

confidence: 99%

Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods

Ziemba

2021

Energies

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All over the world, including Poland, authorities are taking steps to increase consumer interest in electric vehicles and sustainable transport as a way to reduce environmental pollution. For this reason, the electric vehicle market is dynamically and constantly developing, more and more modern vehicles are introduced to it, and purchases are often subsidized by the government. The aim of the article is to analyse the A–C segments of the Polish electric vehicle market and to recommend the most attractive vehicle from the perspective of sustainable transport. The aim of the research was achieved with the use of three multi-criteria decision aid (MCDA) methods, which deal well with the uncertainty and imprecision of data that occur in the case of many different parameters of electric vehicles. In particular, the following methods were used: the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), the fuzzy simple additive weighting (SAW) method, and the new easy approach to fuzzy preference ranking organization method for enrichment evaluation II (NEAT F-PROMETHEE II). Electric vehicle rankings obtained using each method were compared and verified by stochastic analysis. The conducted analyses and comparisons allowed us to identify the most interesting electric vehicles, which currently appear to be the Volkswagen ID.3 Pro S and Nissan LEAF e+.

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Forecasting and Assessment of the Energy Security Risk in Fuzzy Environment

Cited by 10 publications

References 62 publications

Energy Security Assessment Based on a New Dynamic Multi-Criteria Decision-Making Framework

Energy Security Assessment Based on a New Dynamic Multi-Criteria Decision-Making Framework

Managing Economic Uncertainty: Fuzzy Computational Models in International Oil Economy Forecasting

Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods

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