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Volumn , Issue , 2007, Pages 342-346

Short-term wind energy forecasting

Author keywords

Autoregressive integrated moving average processes; Energy management; Feedforward neural networks; Moving average processes; Wind energy

Indexed keywords

ATMOSPHERICS; ELECTRIC POWER SUPPLIES TO APPARATUS; ENERGY CONSERVATION; ENERGY RESOURCES; FORECASTING; FOSSIL FUEL POWER PLANTS; FOSSIL FUELS; MATHEMATICAL MODELS; PARKS; STANDARDS; WEATHER FORECASTING; WEATHER INFORMATION SERVICES; WIND POWER;

EID: 49249105178     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EPC.2007.4520354     Document Type: Conference Paper
Times cited : (8)

References (11)
  • 6
    • 0034286972 scopus 로고    scopus 로고
    • A Comparison of various forecasting techniques applied to mean hourly wind speed time series
    • A. Sfetsos, A Comparison of various forecasting techniques applied to mean hourly wind speed time series. International Journal on Renewable Energy, 21, 2000, 23-35.
    • (2000) International Journal on Renewable Energy , vol.21 , pp. 23-35
    • Sfetsos, A.1
  • 7
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • DE. Rumelhart, GE. Hinton, RJ, Williams, Learning representations by back-propagating errors, Nature, 323(9), 1986, 533-536.
    • (1986) Nature , vol.323 , Issue.9 , pp. 533-536
    • DE1    Rumelhart2    GE3    Hinton4    RJ5    Williams6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.