Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.
BarqueMMartinSVianinJEN, et al. (2018) Improving wind power prediction with retraining machine learning algorithms. In: 2018 international workshop on big data and information security (IWBIS), Jakarta, Indonesia, 12–13 May 2018, pp.43–48. New York: IEEE.
2.
DhimanHSDebDBalasVE (2020) Supervised machine learning in wind forecasting and ramp event prediction. London: Academic Press.
3.
DudhiaJ (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences46(20): 3077–3107.
4.
ErdemEShiJ (2011) ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy88(4): 1405–1414.
5.
FitchACOlsonJBLundquistJK, et al. (2012) Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Monthly Weather Review140(9): 3017–3038.
6.
GalaYFernándezÁDíazJ, et al. (2016) Hybrid machine learning forecasting of solar radiation values. Neurocomputing176: 48–59.
7.
GhaderiASanandajiBMGhaderiF (2017) Deep forecast: Deep learning-based spatio-temporal forecasting. arXiv Preprint. arXiv:1707.08110.
8.
GiebelGBrownswordRKariniotakisG, et al. (2011) The State-of-the-Art in Short-Term Prediction of Wind Power: A Literature Overview, 2nd edn. ANEMOS.plus.
9.
H2O.ai. (2020) h2o: R Interface for H2O. R package version 3.30.0.1. Available at: https://github.com/h2oai/h2o-3. [dataset] Özen, Cem; Dinç, Umur (2020), “Wind Power Generation Forecast by Coupling Numerical Weather Prediction Model and Gradient Boosting Machines in Yahyalı Wind Power Plant”, Mendeley Data, V4, doi:10.17632/wg9pkrp3rv.4
10.
HahmannANPianALennardC, et al. (2018) Mesoscale Modelling for the Wind Atlas of South Africa (WASA) Project–Phase II. Denmark: DTU Wind Energy.
11.
HongSYLimJOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pacific Journal of Atmospheric Sciences42(2): 129–151.
JiménezPADudhiaJGonzález-RoucoJF, et al. (2012) A revised scheme for the WRF surface layer formulation. Monthly Weather Review140(3): 898–918.
14.
JungJBroadwaterRP (2014) Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews31: 762–777.
15.
KainJS (2004) The Kain–Fritsch convective parameterization: An update. Journal of Applied Meteorology43(1): 170–181.
16.
LahouarASlamaJBH (2017) Hour-ahead wind power forecast based on random forests. Renewable Energy109: 529–541.
17.
LazardN (2019) Lazard’s levelized cost of energy analysis–Version13.0.
18.
MlawerEJTaubmanSJBrownPD, et al. (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research: Atmospheres102(D14): 16663–16682.
19.
Mukul TewariNCARTewariMChenF, et al. (2004) Implementation and verification of the unified NOAH land surface model in the WRF model (Formerly Paper Number 17.5). In: Proceedings of the 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, Seattle, WA, pp.11–15.
20.
NakanishiMNiinoH (2006) An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Boundary-Layer Meteorology119(2): 397–407.
21.
NatekinAKnollA (2013) Gradient boosting machines, a tutorial. Frontiers in neurorobotics7: 21.
22.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce (2015a) NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, doi:10.5065/D65D8PWK, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated daily.) (accessed 25 December 2019).
23.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce (2015b), NCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids, doi:10.5065/D65Q4T4Z, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated daily.) (accessed 11 January 2020).
24.
Otero-CasalCPatlakasPPrósperMA, et al. (2019) Development of a high-resolution wind forecast system based on the WRF model and a hybrid Kalman-Bayesian filter. Energies12(16): 3050.
25.
PrósperMAOtero-CasalCFernándezFC, et al. (2019) Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations. Renewable Energy135: 674–686.
26.
RodriguesEROliveiraICunhaR, et al. (2018) DeepDownscale: A deep learning strategy for high-resolution weather forecast. In: 2018 IEEE 14th international conference on e-science (e-Science), Amsterdam, Netherlands, 29 October–1 November 2018, pp.415–422. New York: IEEE.
27.
Salcedo-SanzSCornejo-BuenoLPrietoL, et al. (2018) Feature selection in machine learning prediction systems for renewable energy applications. Renewable and Sustainable Energy Reviews90: 728–741.
28.
SkamarockWCKlempJBDudhiaJ, et al. (2019) A description of the advanced research WRF version 4 (No. NCAR/TN-556+ STR). National Center For Atmospheric Research Boulder Co Mesoscale and Microscale Meteorology Div.
29.
SweeneyCBessaRJBrowellJ, et al. (2020) The future of forecasting for renewable energy. Wiley Interdisciplinary Reviews: Energy and Environment9(2): e365.
30.
TianZ (2020a) A state-of-the-art review on wind power deterministic prediction. Wind Engineering. Epub ahead of print 14July2020. DOI: 10.1177/0309524X20941203.
31.
TianZ (2020b) Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM. Engineering Applications of Artificial Intelligence91: 103573.
32.
TianZLiSWangY (2020a) A prediction approach using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine for short-term wind speed. Wind Energy23(2): 177–206.
33.
TianZLiSWangY, et al. (2018a) Wind power prediction method based on hybrid kernel function support vector machine. Wind Engineering42(3): 252–264.
34.
TianZRenYWangG (2018b) Short-term wind power prediction based on empirical mode decomposition and improved extreme learning machine. Journal of Electrical Engineering & Technology13(5): 1841–1851.
35.
TianZRenYWangG (2020b) An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed. Wind Engineering44(3): 266–281.
36.
TianZWangGRenY (2020c) Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation. Wind Engineering44(2): 152–167.
37.
TianZWangGLiS, et al. (2019) Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction. Wind Engineering43(3): 263–276.
38.
WangXGuoPHuangX (2011) A review of wind power forecasting models. Energy Procedia12: 770–778.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
