Hybrid Model Combined Fuzzy Multi-Objective Decision Making with Feed Forward Neural Network (F-MODM-FFNN) For Very Short-Term Load Forecasting Based on Weather Data.

Autor:	Yundra, Eppy, Kartini, Unit Three, Wardani, Laili Ika, Ardianto, Dwi
Předmět:	LOAD forecasting (Electric power systems) FUZZY decision making WEATHER forecasting STANDARD deviations METEOROLOGY ARTIFICIAL neural networks DATABASES
Zdroj:	International Journal of Intelligent Engineering & Systems; 2020, Vol. 13 Issue 4, p182-195, 14p
Abstrakt:	This research paper proposes a new hybrid methodology for very short-term load forecasting of hourly or designed to predict load for 1 hour ahead. The proposed hybrid methodology is based on weather data especially for optimizing the operation of power generating electricity from thermal generation. This hybrid modelling is a combination of the Fuzzy-Multi-Objective Decision-Making and feed-forward-Neural Network method. The novelty of this hybrid model is taking into account the weather and meteorology data. The first hybrid model implements Fuzzy-Multi Objective Decision Making as an input data pre-processing technique before the feed-forward Neural Network model. The error statistical indicators of the Fuzzy-Multi Objective Decision Making-Feed Forward Neural Network) model for mean square error is the mean value error 0.001 and root mean square error is mean value error 0.2. Note that the highest root means square error was 4.43 MW and the mean square error was 837.8 MW during the sixteen periods. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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