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Artificial intelligence‐based wind forecasting using variational mode decomposition
Author(s) -
V Vanitha,
G Sophia J.,
R Resmi,
Raphel Delna
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12331
Subject(s) - wind speed , intermittency , wind power , mode (computer interface) , adaptive neuro fuzzy inference system , computer science , hilbert–huang transform , energy (signal processing) , meteorology , fuzzy logic , artificial intelligence , mathematics , engineering , statistics , fuzzy control system , geography , electrical engineering , turbulence , operating system
Intermittency in wind offers the major challenge in accomplishing the wind energy as a dependable sustainable energy resource in power grid. Fluctuations in wind speed occur seasonally over a year and if this seasonality is considered, the prediction of the speed of wind can be made more accurate. In this paper, an attempt is made to apply a signal decomposition technique called Variational Mode Decomposition (VMD), which decomposes series of wind speed data into several intrinsic mode functions (IMFs) to make the data more regular thereby enhancing the accuracy of the wind speed forecast model. Then, artificial intelligence technique, Adaptive neuro fuzzy inference system (ANFIS) is applied for the wind speed prediction by combining the obtained modes from VMD. Here, wind data of two sites in India, Jogimatti and Lamba are taken for the study. Each site data is grouped into high and low wind speed months and later, this series is decomposed into regular modes using VMD. Later, ANFIS is applied for training and predicting the wind speed for different time horizons.