Wind Speed Forecasting Using Recurrent Neural Networks and Long Short Term Memory

Autor:	Fitriana R. Ningsih, Asep Najmurrakhman, Esmeralda C. Djamal
Rok vydání:	2019
Předmět:	Training set Java Computer science business.industry Human life Machine learning computer.software_genre Wind speed Variable (computer science) Long short term memory Recurrent neural network Natural phenomenon Artificial intelligence business computer computer.programming_language
Zdroj:	2019 6th International Conference on Instrumentation, Control, and Automation (ICA).
DOI:	10.1109/ica.2019.8916717
Popis:	Wind is a natural phenomenon that plays an essential role in various aspects of human life, including the spread of pests in plants. This variable is right for regions often hit by strong winds. The development of machine learning technology now makes predictions of wind speed to anticipate future impacts. This study proposes wind speed predictions using Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM). The data used was obtained from the Nganjuk Meteorology and Geophysics Agency (BMKG), East Java from 2008 to 2017. The results showed that the use of the Adam model could provide 92.7% accuracy for training data and 91.6% for new data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1bd398070c23bdcc91e6d42c4aac83a5 https://doi.org/10.1109/ica.2019.8916717 Zobrazit plný text záznamu