TY - GEN
T1 - The application of neural network in wind speed forecasting
AU - Huang, Shih Hua
AU - Mu, Ko Ming
AU - Lu, Ping Yuan
AU - Tsao, Chao Yang
AU - Leu, Yih Guang
AU - Chou, Li Fen
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Usually, a typical wind turbine system cannot generate electricity power consistently, because the power outputs are heavily depended on wind speed. However, terrain, temperature, humidity and other factors can also affect wind speed. Therefore, wind power forecasting is a complex, multi-dimensional, and highly non-linear system. Neural network is able to learn the relationship between system inputs and outputs without mathematical conversion, and perform complex non-linear mapping, data classification, knowledge processing, and so forth. In addition, neural network also has the ability of parallel processing to reduce computing time, so it is suitable for wind power forecasting. The purpose of this paper is to use neural network technology to design a wind power forecasting system. Moreover, the efficiency analysis of the proposed wind power forecasting system in Kinmen farm is described. Finally, we use MATLAB to implement the proposed wind power forecasting system in Kinmen farm, which is capable of forecast within 48-hours ahead.
AB - Usually, a typical wind turbine system cannot generate electricity power consistently, because the power outputs are heavily depended on wind speed. However, terrain, temperature, humidity and other factors can also affect wind speed. Therefore, wind power forecasting is a complex, multi-dimensional, and highly non-linear system. Neural network is able to learn the relationship between system inputs and outputs without mathematical conversion, and perform complex non-linear mapping, data classification, knowledge processing, and so forth. In addition, neural network also has the ability of parallel processing to reduce computing time, so it is suitable for wind power forecasting. The purpose of this paper is to use neural network technology to design a wind power forecasting system. Moreover, the efficiency analysis of the proposed wind power forecasting system in Kinmen farm is described. Finally, we use MATLAB to implement the proposed wind power forecasting system in Kinmen farm, which is capable of forecast within 48-hours ahead.
KW - Kinmen farm
KW - neural network
KW - power forecasting
KW - wind
UR - http://www.scopus.com/inward/record.url?scp=84941195996&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941195996&partnerID=8YFLogxK
U2 - 10.1109/ICNSC.2015.7116064
DO - 10.1109/ICNSC.2015.7116064
M3 - Conference contribution
AN - SCOPUS:84941195996
T3 - ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control
SP - 366
EP - 370
BT - ICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015
Y2 - 9 April 2015 through 11 April 2015
ER -