Restricted accessResearch articleFirst published online 2022-10
Short-term wind speed forecast for Urla wind power plant: A hybrid approach that couples weather research and forecasting model,weather patterns and SCADA data with comprehensive data preprocessing
Short-term wind speed forecast model that uses both supervisory control and data acquisition (SCADA) based data and weather research and forecasting (WRF) model outputs for Urla wind power plant (WPP) has been proposed in this study. Two different WRF models were run to gather atmospheric variables from four surrounding grids of Urla WPP and calculate weather patterns affecting Urla WPP. After detecting outliers in the SCADA data by coupling of k-mean and isolation forest (IF) methods, statistical methods were used for data treatment and the outputs of WRF models were used for missing data imputation. The effect of each data type and data preprocessing techniques on the model was evaluated separately. The best model performance was achieved with 0.9085 , and 0.81 MAE in the dataset which includes each data type and each data preprocessing was applied on. Otherwise, the dominant weather pattern affecting Urla WPP was found to be purely advective and the best result was achieved in this pattern.
CadenasEJaramilloOARiveraW (2010) Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method. Renewable Energy35(5): 925–930. https://doi.org/10.1016/j.renene.2009.10.037
ClevelandWSGrosseEShyuWM (1992) Local regression models. In: ChambersJMHastieTJ (eds.) Chapter 8 in Statistical models in S.Pacific Grove, CA: Wadsworth & Brooks/Cole, p.608.
5.
DongYZhangHWangC, et al. (2021) Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm. Neurocomputing462: 169–184. https://doi.org/10.1016/j.neucom.2021.07.084
6.
DudhiaJ (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of Atmospheric Sciences46(20): 3077–3107.
7.
FoleyAMLeahyPGMarvugliaA, et al. (2012) Current methods and advances in forecasting of wind power generation. Renewable energy37(1): 1–8. https://doi.org/10.1016/j.renene.2011.05.033
8.
GuoHWangJLiZ, et al. (2021) A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization. Energy239: 122333. https://doi.org/10.1016/j.energy.2021.122333
Heidrich-MeisnerVWimmer-SchweingruberRF (2018) Solar wind classification via k-means clustering algorithm. In: CamporealeEWingSJohnsonJR (eds) Machine learning techniques for space weather, pp. 397–424. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-0-12-811788-0.00016-0
11.
HongSYLimJOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pacific Journal of Atmospheric Sciences42(2): 129–151.
12.
JacondinoWDda Silva NascimentoALCalvettiL, et al. (2021) Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model. Energy230: 120841. https://doi.org/10.1016/j.energy.2021.120841
JiménezPADudhiaJGonzález-RoucoJF, et al. (2012) A revised scheme for the WRF surface layer formulation. Monthly Weather Review140(3): 898–918. https://doi.org/10.1175/MWR-D-11-00056.1
15.
JonesPDHarphamCBriffaKR (2013) Lamb weather types derived from reanalysis products. International Journal of Climatology33(5): 1129–1139. https://doi.org/10.1002/joc.3498
16.
JonesPDHulmeMBriffaKR (1993) A comparison of Lamb circulation types with an objective classification scheme. International Journal of Climatology13(6): 655–663.
KisvariALinZLiuX (2021) Wind power forecasting–A data-driven method along with gated recurrent neural network. Renewable Energy163: 1895–1909. https://doi.org/10.1016/j.renene.2020.10.119
21.
LambHH (1972) British Isles weather types and a register of the daily sequence of circulation patterns 1861–1971. London: H.M. Stationery Office.
22.
LinZLiuXColluM (2020) Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. International Journal of Electrical Power & Energy Systems118: 105835. https://doi.org/10.1016/j.ijepes.2020.105835
23.
LiuFTTingKMZhouZH (2008) Isolation forest. In: Eighth IEEE International Conference on Data Mining, December2008, pp.413–422. Piscataway, NJ: IEEE. https://doi.org/10.1109/ICDM.2008.17
24.
LiuHChenCLvX, et al. (2019) Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management195: 328–345. https://doi.org/10.1016/j.enconman.2019.05.020
25.
LiuXLinZFengZ (2021) Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM. Energy227: 120492. https://doi.org/10.1016/j.energy.2021.120492
26.
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. https://doi.org/10.1029/97JD00237
27.
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: 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, Seattle, WA, USA, January 2004, pp. 11–15.
28.
NakanishiMNiinoH (2009) Development of an improved turbulence closure model for the atmospheric boundary layer. Journal of the Meteorological Society of Japan Series II87(5): 895–912.
29.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2015, updated daily. NCEP GDAS/FNL 0.25 degree global tropospheric analyses and forecast grids. Research data archive at the national center for atmospheric research, computational and information systems laboratory. https://doi.org/10.5065/D65Q4T4Z (accessed 5 September 2021).
30.
NoorollahiYJokarMAKalhorA (2016) Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Conversion and Management115: 17–25. https://doi.org/10.1016/j.enconman.2016.02.041
31.
OkumusIDinlerA (2016) Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Conversion and Management123: 362–371. https://doi.org/10.1016/j.enconman.2016.06.053
32.
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. https://doi.org/10.3390/en12163050
33.
ÖzenCDinçUDenizA, et al. (2020) Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant. Wind Engineering45(5): 1256–1272. https://doi.org/10.1177/0309524X20972115
34.
PowersJGKlempJBSkamarockWC, et al. (2017) The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society98(8): 1717–1737. https://doi.org/10.1175/BAMS-D-15-00308.1
35.
ProkhorenkovaLGusevGVorobevA, et al. (2017) CatBoost: Unbiased boosting with categorical features. arXiv preprint:1706.09516. https://arxiv.org/abs/1706.09516
36.
RenGLiuJWanJ, et al. (2017) Overview of wind power intermittency: Impacts, measurements, and mitigation solutions. Applied Energy204: 47–65. https://doi.org/10.1016/j.apenergy.2017.06.098
37.
RuizSAGBarrigaJECMartínezJA (2021) Wind power assessment in the Caribbean region of Colombia, using ten-minute wind observations and ERA5 data. Renewable Energy172: 158–176. https://doi.org/10.1016/j.renene.2021.03.033
38.
ShaoHDengXCuiF (2016) Short-term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China. IET Generation, Transmission & Distribution10(11): 2585–2592. https://doi.org/10.1049/iet-gtd.2015.0911
39.
ShenWChenXQiuJ, et al. (2020) A comprehensive review of variable renewable energy levelized cost of electricity. Renewable and Sustainable Energy Reviews133: 110301. https://doi.org/10.1016/j.rser.2020.110301
SkamarockWCKlempJBDudhiaJ, et al. (2005) A description of the advanced research WRF Version 2 (No. NCAR/TN-468+STR). University Corporation for Atmospheric Research. https://doi.org/10.5065/D6DZ069T
42.
SkamarockWCKlempJBDudhiaJ, et al. (2019) A description of the advanced research WRF model version 4. Boulder, CO: National Center for Atmospheric Research, p.145. https://doi.org/10.5065/1dfh-6p97
43.
SomanSSZareipourHMalikO, et al. (2010) A review of wind power and wind speed forecasting methods with different time horizons. In: IEEE North American Power Symposium 2010, Arlington, TX, USA, pp.1–8. https://doi.org/10.1109/NAPS.2010.5619586
44.
Turkish Wind Energy Association (2021) WPP Database, https://tureb.com.tr (accessed 7 October 2021).
45.
WanZXuYŠavijaB (2021) On the use of machine learning models for prediction of compressive strength of concrete: Influence of dimensionality reduction on the model performance. Materials14(4): 713. https://doi.org/10.3390/ma14040713
46.
WangKQiXLiuH, et al. (2018) Deep belief network based k-means cluster approach for short-term wind power forecasting. Energy165: 840–852. https://doi.org/10.1016/j.energy.2018.09.118
47.
WidénJ (2011) Correlations between large-scale solar and wind power in a future scenario for Sweden. IEEE Transactions on Sustainable Energy2(2): 177–184. https://doi.org/10.1109/TSTE.2010.2101620
48.
WoodSNPyaNSäfkenB (2016) Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association111(516): 1548–1563. https://doi.org/10.1080/01621459.2016.1180986
49.
ZhangJYanJInfieldD, et al. (2019) Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Applied Energy241: 229–244. https://doi.org/10.1016/j.apenergy.2019.03.044
50.
ZhouJShiJLiG (2011) Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management52(4): 1990–1998. https://doi.org/10.1016/j.enconman.2010.11.007
51.
ZuluagaCDAlvarezMAGiraldoE (2015) Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison. Applied Energy156: 321–330. https://doi.org/10.1016/j.apenergy.2015.07.043
