Artificial neural network-based methodology for short-term electric load scenario generation

Autor:	Stylianos I. Vagropoulos, Evaggelos G. Kardakos, Anastasios G. Bakirtzis, Christos K. Simoglou, Joao P. S. Catalao
Rok vydání:	2015
Předmět:	Reduction (complexity) Electric power system Engineering Electrical load Artificial neural network Stochastic process business.industry Control engineering Time series business Simulation Term (time) Power (physics)
Zdroj:	2015 18th International Conference on Intelligent System Application to Power Systems (ISAP).
DOI:	10.1109/isap.2015.7325540
Popis:	In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is able to create scenarios for various power system-related stochastic variables. Scenario reduction methodologies can then be applied to effectively reduce the number of scenarios. An application of the methodology for the creation of short-term electric load scenarios for one day up to seven days ahead is presented. Test results on the real-world insular power system of Crete present the effectiveness of the proposed methodology.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cf978e4b04713ccdbafb8af68e5b096b https://doi.org/10.1109/isap.2015.7325540 Zobrazit plný text záznamu