TY - GEN
T1 - Wind Speed Forecasting Using ARMA and Neural Network Models
AU - Zaman, Uzair
AU - Teimourzadeh, Hamid
AU - Sangani, Elias Hassani
AU - Liang, Xiaodong
AU - Chung, Chi Yung
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/11
Y1 - 2021/11
N2 - With the advancement of wind power generation technology, wind power plays an increasing role in modern power grids. To properly consider wind power for power systems planning and operation purpose, wind power and wind speed must be forecasted accurately. Wind is chaotic, random, irregular, and non-stationary in nature, which creates significant challenges in wind speed forecasting. This paper aims to forecast wind speed using both the statistical time series analysis method (autoregressive moving average (ARMA)) and neural network methods (feedforward neural network (FNN), recurrent neural network (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU)). The performance of the proposed five models is compared with the measured wind speed data, and the GRU model shows the best performance with the highest prediction accuracy. The four ANN models outperform the ARMA model.
AB - With the advancement of wind power generation technology, wind power plays an increasing role in modern power grids. To properly consider wind power for power systems planning and operation purpose, wind power and wind speed must be forecasted accurately. Wind is chaotic, random, irregular, and non-stationary in nature, which creates significant challenges in wind speed forecasting. This paper aims to forecast wind speed using both the statistical time series analysis method (autoregressive moving average (ARMA)) and neural network methods (feedforward neural network (FNN), recurrent neural network (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU)). The performance of the proposed five models is compared with the measured wind speed data, and the GRU model shows the best performance with the highest prediction accuracy. The four ANN models outperform the ARMA model.
KW - Artificial Neural Network
KW - autoregressive moving average
KW - gated recurrent unit
KW - wind speed foresting
UR - http://www.scopus.com/inward/record.url?scp=85123582304&partnerID=8YFLogxK
U2 - 10.1109/EPEC52095.2021.9621650
DO - 10.1109/EPEC52095.2021.9621650
M3 - Conference article published in proceeding or book
AN - SCOPUS:85123582304
T3 - 2021 IEEE Electrical Power and Energy Conference, EPEC 2021
SP - 243
EP - 248
BT - 2021 IEEE Electrical Power and Energy Conference, EPEC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Electrical Power and Energy Conference, EPEC 2021
Y2 - 22 October 2021 through 31 October 2021
ER -