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Wind energy penetration has increased significantly and is playing a crucial role in the conversion of power systems to renewable energy. Remote and isolated power systems are increasing wind generation due to high cost of diesel fuel and transportation. To address the concerns of system frequency and scheduling from high penetration of stochastic wind generation, accurate short-term wind power forecasting is required. The research Investigates temporal resolution of wind energy data to improve neural network based forecast models. High resolution wind power data is used to simulate different temporal resolution, for both 10 minute and 1 hour forecast horizons. Three decomposition methods are compared wavelet, empirical mode, and variable mode decomposition. They each decomposed the sampled data into different modes, firstly a long-term component of lower frequencies, then more modes with detailed higher frequency components. To evaluate the temporal resolution and decomposition methods Back propagation neural network (BP), long short-term memory neural network (LSTM) and a convolutional neural network (CNN) are evaluated using wind power data from the King Island power system.

Item Type:	Refereed Conference Paper
Keywords:	temporal resolution, renewable energy
Research Division:	Engineering
Research Group:	Electrical engineering
Research Field:	Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:	Energy
Objective Group:	Energy efficiency
Objective Field:	Industrial energy efficiency
UTAS Author:	Aitken, W (Mr William Aitken)
UTAS Author:	Negnevitsky, M (Professor Michael Negnevitsky)
UTAS Author:	Semshchikov, E (Mr Evgenii Semshikov)
ID Code:	142988
Year Published:	2020
Deposited By:	Engineering
Deposited On:	2021-02-18
Last Modified:	2021-03-03
Downloads:	26 View Download Statistics