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Volumn 8, Issue 4, 2017, Pages 1571-1580

Improving Renewable Energy Forecasting with a Grid of Numerical Weather Predictions

Author keywords

Feature engineering; forecasting; probabilistic; solar energy; spatial; temporal; weather predictions; wind energy

Indexed keywords

FORECASTING; SOLAR ENERGY; TREES (MATHEMATICS); WIND POWER;

EID: 85027151564     PISSN: 19493029     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSTE.2017.2694340     Document Type: Article
Times cited : (235)

References (32)
  • 2
    • 84958254280 scopus 로고    scopus 로고
    • Solar forecasting: Methods, challenges, and performance
    • Nov./Dec
    • A. Tuohy et al., "Solar forecasting: Methods, challenges, and performance," IEEE Power Energy Mag., vol. 13, no. 6, pp. 50-59, Nov./Dec. 2015.
    • (2015) IEEE Power Energy Mag. , vol.13 , Issue.6 , pp. 50-59
    • Tuohy, A.1
  • 3
    • 84867985267 scopus 로고    scopus 로고
    • Regional wind power forecasting based on smoothing techniques, with application to the Spanish peninsular system
    • Nov
    • M. G. Lobo and I. Sánchez, "Regional wind power forecasting based on smoothing techniques, with application to the spanish peninsular system," IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1990-1997, Nov. 2012.
    • (2012) IEEE Trans. Power Syst. , vol.27 , Issue.4 , pp. 1990-1997
    • Lobo, M.G.1    Sánchez, I.2
  • 4
    • 84898018026 scopus 로고    scopus 로고
    • Optimal voltage control using inverters interfaced with PV systems considering forecast error in a distribution system
    • Apr
    • Z. Ziadi et al., "Optimal voltage control using inverters interfaced with PV systems considering forecast error in a distribution system." IEEE Trans. Sustain. Energy, vol. 5, no. 2, pp. 682-690, Apr. 2014.
    • (2014) IEEE Trans. Sustain. Energy , vol.5 , Issue.2 , pp. 682-690
    • Ziadi, Z.1
  • 5
    • 70049096782 scopus 로고    scopus 로고
    • Online short-term solar power forecasting
    • Oct
    • P. Bacher, H. Madsen, and H. A. Nielsen, "Online short-term solar power forecasting," Sol. Energy, vol. 83, no. 10, pp. 1772-1783, Oct. 2009.
    • (2009) Sol. Energy , vol.83 , Issue.10 , pp. 1772-1783
    • Bacher, P.1    Madsen, H.2    Nielsen, H.A.3
  • 7
    • 14344255817 scopus 로고    scopus 로고
    • Probabilistic wind power forecasts using local quantile regression
    • Jan.-Mar
    • J. B. Bremnes, "Probabilistic wind power forecasts using local quantile regression," Wind Energy, vol. 7, no. 1, pp. 47-54, Jan.-Mar. 2004.
    • (2004) Wind Energy , vol.7 , Issue.1 , pp. 47-54
    • Bremnes, J.B.1
  • 8
    • 84883215425 scopus 로고    scopus 로고
    • Time adaptive conditional kernel density estimation for wind power forecasting
    • Oct
    • R. Bessa, V. Miranda, A. Botterud, J. Wang, and E. M. Constantinescu, "Time adaptive conditional kernel density estimation for wind power forecasting," IEEE Trans. Sustain. Energy, vol. 3, no. 4, pp. 660-669, Oct. 2012.
    • (2012) IEEE Trans. Sustain. Energy , vol.3 , Issue.4 , pp. 660-669
    • Bessa, R.1    Miranda, V.2    Botterud, A.3    Wang, J.4    Constantinescu, E.M.5
  • 9
    • 84919658679 scopus 로고    scopus 로고
    • A novel application of an analog ensemble for short-term wind power forecasting
    • Apr
    • S. Alessandrini, L. D. Monache, S. Sperati, and J. Nissen, "A novel application of an analog ensemble for short-term wind power forecasting," Renew. Energy, vol. 76, no. 768-781, Apr. 2015.
    • (2015) Renew. Energy , vol.76 , Issue.768-781
    • Alessandrini, S.1    Monache, L.D.2    Sperati, S.3    Nissen, J.4
  • 10
    • 84939814087 scopus 로고    scopus 로고
    • An analog ensemble for short-term probabilistic solar power forecast
    • Nov
    • S. Alessandrini, L. D. Monache, S. Sperati, and G. Cervone, "An analog ensemble for short-term probabilistic solar power forecast," Appl. Energy, vol. 157, pp. 95-110, Nov. 2015.
    • (2015) Appl. Energy , vol.157 , pp. 95-110
    • Alessandrini, S.1    Monache, L.D.2    Sperati, S.3    Cervone, G.4
  • 11
    • 84962349410 scopus 로고    scopus 로고
    • K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting
    • Jul.-Sep
    • Y. Zhang and J.Wang, "K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting," Int. J. Forecast., vol. 32, no. 3, pp. 1074-1080, Jul.-Sep. 2016.
    • (2016) Int. J. Forecast. , vol.32 , Issue.3 , pp. 1074-1080
    • Zhang, Y.1    Wang, J.2
  • 13
    • 84892960976 scopus 로고    scopus 로고
    • Current status and future advances for wind speed and power forecasting
    • Mar
    • J. Jung and R. P. Broadwater, "Current status and future advances for wind speed and power forecasting," Renew. Sustain. Energy Rev., vol. 32, pp. 762-777, Mar. 2014.
    • (2014) Renew. Sustain. Energy Rev. , vol.32 , pp. 762-777
    • Jung, J.1    Broadwater, R.P.2
  • 14
    • 84937764904 scopus 로고    scopus 로고
    • Probabilistic solar power forecasting in smart grids using distributed information
    • Nov
    • R. Bessa, A. Trindade, C. Silva, and V. Miranda, "Probabilistic solar power forecasting in smart grids using distributed information," Int. J. Elect. Power Energy Syst., vol. 72, pp. 16-23, Nov. 2015.
    • (2015) Int. J. Elect. Power Energy Syst. , vol.72 , pp. 16-23
    • Bessa, R.1    Trindade, A.2    Silva, C.3    Miranda, V.4
  • 15
    • 84976475766 scopus 로고    scopus 로고
    • Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power
    • Jul
    • A. Tascikaraoglu et al., "Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power," IEEE Trans. Sustain. Energy, vol. 7, no. 3, pp. 1295-1305, Jul. 2016.
    • (2016) IEEE Trans. Sustain. Energy , vol.7 , Issue.3 , pp. 1295-1305
    • Tascikaraoglu, A.1
  • 16
    • 84988346596 scopus 로고    scopus 로고
    • LASSO vector autoregression structures for very short-term wind power forecasting
    • Apr
    • L. Cavalcante, R. J. Bessa, M. Reis, and J. Browell, "LASSO vector autoregression structures for very short-term wind power forecasting," Wind Energy, vol. 20, pp. 657-675, Apr. 2017.
    • (2017) Wind Energy , vol.20 , pp. 657-675
    • Cavalcante, L.1    Bessa, R.J.2    Reis, M.3    Browell, J.4
  • 17
    • 84929191655 scopus 로고    scopus 로고
    • Very-short-term probabilistic wind power forecasts by sparse vector autoregression
    • Mar
    • J. Dowell and P. Pinson, "Very-short-term probabilistic wind power forecasts by sparse vector autoregression," IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 763-770, Mar. 2016.
    • (2016) IEEE Trans. Smart Grid , vol.7 , Issue.2 , pp. 763-770
    • Dowell, J.1    Pinson, P.2
  • 18
    • 84870222552 scopus 로고    scopus 로고
    • Multivariate conditional parametric models for a spatio-temporal analysis of short-term wind power forecast errors
    • Warsaw, Poland, Apr
    • J. Tastu, P. Pinson, and H. Madsen, "Multivariate conditional parametric models for a spatio-temporal analysis of short-term wind power forecast errors," in Proc. Eur. Wind Energy Conf., Warsaw, Poland, Apr. 2010, pp. 1-9.
    • (2010) Proc. Eur. Wind Energy Conf. , pp. 1-9
    • Tastu, J.1    Pinson, P.2    Madsen, H.3
  • 19
    • 84903191929 scopus 로고    scopus 로고
    • A spatio-temporal analysis approach for short-term forecast of wind farm generation
    • Jul
    • M. He, L. Yang, J. Zhang, and V. Vittal, "A spatio-temporal analysis approach for short-term forecast of wind farm generation," IEEE Trans. Power Syst., vol. 29, no. 4, pp. 1611-1622, Jul. 2014.
    • (2014) IEEE Trans. Power Syst. , vol.29 , Issue.4 , pp. 1611-1622
    • He, M.1    Yang, L.2    Zhang, J.3    Vittal, V.4
  • 20
    • 84960172423 scopus 로고    scopus 로고
    • Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond
    • Jul.-Sep
    • T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli, and R. J. Hyndman, "Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond," Int. J. Forecast., vol. 32, no. 3, pp. 896-913, Jul.-Sep. 2016.
    • (2016) Int. J. Forecast. , vol.32 , Issue.3 , pp. 896-913
    • Hong, T.1    Pinson, P.2    Fan, S.3    Zareipour, H.4    Troccoli, A.5    Hyndman, R.J.6
  • 21
    • 84973143544 scopus 로고    scopus 로고
    • GEFCom2014: Probabilistic solar andwind power forecasting using a generalized additive tree ensemble approach
    • Jul.-Sep
    • G. I. Nagy, G. Barta, S. Kazi, G. Borbély, and G. Simon, "GEFCom2014: Probabilistic solar andwind power forecasting using a generalized additive tree ensemble approach," Int. J. Forecast., vol. 32, no. 3, pp. 1087-1093, Jul.-Sep. 2016.
    • (2016) Int. J. Forecast. , vol.32 , Issue.3 , pp. 1087-1093
    • Nagy, G.I.1    Barta, G.2    Kazi, S.3    Borbély, G.4    Simon, G.5
  • 22
    • 84961844695 scopus 로고    scopus 로고
    • Probabilistic gradient boosting machines for GEFCom2014 wind forecasting
    • Jul.-Sep
    • M. Landry, T. P. Erlinger, D. Patschke, and C. Varrichio, "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," Int. J. Forecast., vol. 32, no. 3, pp. 1061-1066, Jul.-Sep. 2016.
    • (2016) Int. J. Forecast. , vol.32 , Issue.3 , pp. 1061-1066
    • Landry, M.1    Erlinger, T.P.2    Patschke, D.3    Varrichio, C.4
  • 23
    • 84950134550 scopus 로고    scopus 로고
    • A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting
    • Jul.-Sep
    • J. Huang and M. Perry, "A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting," Int. J. Forecast., vol. 32, no. 3, pp. 1081-1086, Jul.-Sep. 2016.
    • (2016) Int. J. Forecast. , vol.32 , Issue.3 , pp. 1081-1086
    • Huang, J.1    Perry, M.2
  • 24
    • 84975733502 scopus 로고    scopus 로고
    • A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014
    • Jul.-Sep
    • R. Juban, H. Ohlsson, M. Maasoumy, L. Poirier, and J. Z. Kolter, "A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014," Int. J. Forecast., vol. 32, no. 3, pp. 1094-1102, Jul.-Sep. 2016.
    • (2016) Int. J. Forecast. , vol.32 , Issue.3 , pp. 1094-1102
    • Juban, R.1    Ohlsson, H.2    Maasoumy, M.3    Poirier, L.4    Kolter, J.Z.5
  • 25
    • 85013791122 scopus 로고    scopus 로고
    • A high-accuracy wind power forecasting model
    • Mar
    • S. Fang and H.-D. Chiang, "A high-accuracy wind power forecasting model," IEEE Trans. Power Syst., vol. 32, no. 2, pp. 1589-1590, Mar. 2017.
    • (2017) IEEE Trans. Power Syst. , vol.32 , Issue.2 , pp. 1589-1590
    • Fang, S.1    Chiang, H.-D.2
  • 26
    • 84925395797 scopus 로고    scopus 로고
    • PV power forecast using a nonparametric PV model
    • May
    • M. P. Almeida, O. Perpinán, and L. Narvarte, "PV power forecast using a nonparametric PV model," Sol. Energy, vol. 115, pp. 354-368, May 2015.
    • (2015) Sol. Energy , vol.115 , pp. 354-368
    • Almeida, M.P.1    Perpinán, O.2    Narvarte, L.3
  • 27
    • 84968739482 scopus 로고    scopus 로고
    • Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting
    • Sep
    • F. Davò, S. Alessandrini, S. Sperati, L. D. Monache, D. Airoldi, and M. T. Vespucci, "Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting," Sol. Energy, vol. 134, pp. 327-338, Sep. 2016.
    • (2016) Sol. Energy , vol.134 , pp. 327-338
    • Davò, F.1    Alessandrini, S.2    Sperati, S.3    Monache, L.D.4    Airoldi, D.5    Vespucci, M.T.6
  • 29
    • 84869201485 scopus 로고    scopus 로고
    • Practical Bayesian optimization of machine learning algorithms
    • Lake Tahoe, NV, USA, 3-6 Dec
    • J. Snoek and H. Larochelle, "Practical Bayesian optimization of machine learning algorithms," in Proc. 26th Annu. Conf. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, 3-6 Dec. 2012, pp. 2951-2959.
    • (2012) Proc. 26th Annu. Conf. Neural Inf. Process. Syst. , pp. 2951-2959
    • Snoek, J.1    Larochelle, H.2
  • 30
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Ann. Statist., vol. 29, no. 5, pp. 1189-1232, 2001.
    • (2001) Ann. Statist. , vol.29 , Issue.5 , pp. 1189-1232
    • Friedman, J.H.1
  • 32
    • 80555140075 scopus 로고    scopus 로고
    • Scikit-learn: Machine learning in Python
    • F. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.
    • (2011) J. Mach. Learn. Res. , vol.12 , pp. 2825-2830
    • Pedregosa, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.