Gath-geva approach to forecast electric energy consumption

Autor:	Bouabaz Mohamed, Mordjaoui Mourad, Boudjema Bouzid
Rok vydání:	2013
Předmět:	Engineering Fuzzy clustering Electrical load business.industry Scheduling (production processes) Energy consumption Machine learning computer.software_genre Electric power system Electric energy consumption Probabilistic forecasting Artificial intelligence Electricity Data mining business computer
Zdroj:	4th International Conference on Power Engineering, Energy and Electrical Drives.
DOI:	10.1109/powereng.2013.6635629
Popis:	Short-term load forecasting is necessary for adequate scheduling and operation of power systems. It's generally made by developing models in relations to climate and previous load data. In this paper, we discuss in detail how Fuzzy clustering based on Gath and Geva algorithm is successfully applied to electric load forecasting. Results and discussions from real-world case studies based on data from RTE France of electricity consumption in 2010 are presented. The variance accounted for (VAF) and the RMSE indices used in modeling process are respectively 99.5059 and 0.0545 for training and 85.8950, 0.1872 for validation showing the good prediction performance and the suitability of the proposed approach for short-term electric load forecasting.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c005f27e2ce4dd81bbbdbfd17a2f58f4 https://doi.org/10.1109/powereng.2013.6635629 Zobrazit plný text záznamu