Dynamic model based on Bayesian method for energy security assessment. (1st September 2015)

• Record Type: Journal Article
• Authors: Augutis, Juozas; Krikštolaitis, Ričardas; Pečiulytė, Sigita; Žutautaitė, Inga
• Is Part Of: Energy conversion and management. Volume 101(2015)
• Page Start: 66
• Page End: 72
• Publication Date: 2015-09-01
• Subjects: Energy security -- Dynamic indicator model -- Bayesian method; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2015.05.002
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Methodology for dynamic indicator model construction and forecasting of indicators. Application of dynamic indicator model for energy system development scenarios. Expert judgement involvement using Bayesian method. Abstract: The methodology for the dynamic indicator model construction and forecasting of indicators for the assessment of energy security level is presented in this article. An indicator is a special index, which provides numerical values to important factors for the investigated area. In real life, models of different processes take into account various factors that are time-dependent and dependent on each other. Thus, it is advisable to construct a dynamic model in order to describe these dependences. The energy security indicators are used as factors in the dynamic model. Usually, the values of indicators are obtained from statistical data. The developed dynamic model enables to forecast indicators' variation taking into account changes in system configuration. The energy system development is usually based on a new object construction. Since the parameters of changes of the new system are not exactly known, information about their influences on indicators could not be involved in the model by deterministic methods. Thus, dynamic indicators' model based on historical data is adjusted by probabilistic model with the influence of new factors on indicators using the Bayesian method.