A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Autor:	Chiou-Jye Huang, Ping-Huan Kuo
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	artificial neural network wind speed forecasting wind energy power system energy management Technology
Zdroj:	Energies, Vol 11, Iss 10, p 2777 (2018)
Druh dokumentu:	article
ISSN:	1996-1073
DOI:	10.3390/en11102777
Popis:	To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/2ab4d2a038c8490cb303bf9528a5fc7e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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