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Volumn 32, Issue 3, 2016, Pages 1029-1037

Lasso estimation for GEFCom2014 probabilistic electric load forecasting

Author keywords

Probabilistic forecasting; Threshold AR; Time varying effects

Indexed keywords


EID: 84964330415     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2016.01.001     Document Type: Article
Times cited : (83)

References (11)
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  • 5
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    • Hong, T.1    Wang, P.2    White, L.3
  • 6
    • 84892608360 scopus 로고    scopus 로고
    • Long term probabilistic load forecasting and normalization with hourly information
    • Hong, T., Wilson, J., Xie, J., Long term probabilistic load forecasting and normalization with hourly information. IEEE Transactions on Smart Grid 5:1 (2014), 456–462.
    • (2014) IEEE Transactions on Smart Grid , vol.5 , Issue.1 , pp. 456-462
    • Hong, T.1    Wilson, J.2    Xie, J.3
  • 8
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    • Electric load forecasting with recency effect: a big data approach
    • Wang, P., Liu, B., Hong, T., Electric load forecasting with recency effect: a big data approach. International Journal of Forecasting 32 (2016), 585–597.
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  • 9
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    • On normality assumption in residual simulation for probabilistic load forecasting
    • (in press)
    • Xie, J., Hong, T., Laing, D.T., Kang, C., On normality assumption in residual simulation for probabilistic load forecasting. IEEE Transactions on Smart Grid, 2015, 10.1109/TSG.2015.2447007 (in press).
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    • Xie, J.1    Hong, T.2    Laing, D.T.3    Kang, C.4
  • 10
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    • Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to ar-arch type processes
    • (in press)
    • Ziel, F., Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to ar-arch type processes. Computational Statistics and Data Analysis, 2015, 10.1016/j.csda.2015.11.016 (in press).
    • (2015) Computational Statistics and Data Analysis
    • Ziel, F.1
  • 11
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    • Efficient modeling and forecasting of electricity spot prices
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.