Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Although there are many locations suitable to construct new wind turbines, wind speeds in those areas are not always available, which makes it difficult to plan and develop a proper wind energy conversion system. This paper proposes an approach to determine the wind speeds corresponding to locations without any past wind speed data. First, monthly mean wind speed was modeled as a function of geographical variables (latitude, longitude and elevation), atmospheric variables (ambient temperature, atmospheric pressure, percent relative humidity), and the month of the year for a case location (Aegean Region of Turkey) by artificial neural networks (ANNs) trained by the data supplied by 55 wind speed measuring stations throughout the region (660 data points). Then, the prediction ability of the ANN model was tested: The wind speed data of each station was excluded from the database, and an ANN model trained by the data of the rest of the wind stations was used to forecast the excluded data. Finally, a grid search algorithm was applied to the entire region to search for the optimum location for the maximum average annual wind speed which was found to be 10.6m/s. A generic wind turbine was considered at this location and a power of 1.79MW was achieved.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks, Geographical Variables, Wind Speed Prediction, Renewable Energy, Forecasting, Grid Search, Empirical Mode Decomposition, Reference Stations Data, Spatial Estimation, Target Station, Wavelet
Kaynak
Neural Computing & Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
30
Sayı
10
