In recent years, renewable energy has received rapidly growing attention due to its eco-friendly and sustainable properties. Taiwan as an island nation that is planning to develop offshore wind power to reduce the dependence on imported energy. Due to the intermittent and variable nature of wind, a study on wind characteristics and forecasting will make it possible to obtain valuable information on local wind conditions and enhance local forecasting abilities. In this study, wind data from 2017 to 2019, obtained from the Taipower Meteorological Mast in the Taiwan Strait, was used to develop a short-term multistep wind forecasting model. This model was based on a combination of an artificial neural network and a Long Short-Term Memory (LSTM) models. The results revealed that the northeast winds in winter and autumn were steadier, in terms of both speed and direction, than those in spring and summer. The prediction accuracies of this three-step forecasting model reached 0.991, 0.981, and 0.970, respectively. These findings will greatly improve our ability to forecast this important Taiwan Strait wind resource.
AbuellaMChowdhuryB (2017) Random forest ensemble of support vector regression models for solar power forecasting. In: 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington DC, USA, 23–26 April, pp.1–5. New York, NY: IEEE.
2.
Ben TaiebSSorjamaaABontempiG (2010) Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing73(10–12): 1950–1957.
ChenKYuJ (2014) Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Applied Energy113: 690–705.
5.
ChenNQianZNabneyIT, et al. (2014) Wind power forecasts using Gaussian processes and numerical weather prediction. IEEE Transactions on Power Systems29(2): 656–665.
6.
ChungJ.GulcehreC.ChoK.BengioY. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014.
EdigerVŞ (2019) An integrated review and analysis of multi-energy transition from fossil fuels to renewables. Energy Procedia156: 2–6.
11.
ErdemEShiJ (2011) ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy88(4): 1405–1414.
12.
EzzatAAJunMDingY (2018) Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting. IEEE Transactions on Sustainable Energy9(3): 1437–1447.
13.
HuY-LChenL (2018) A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm. Energy Conversion and Management173(1): 123–142.
14.
LahouarABen Hadj SlamaJ (2017) Hour-ahead wind power forecast based on random forests. Renewable Energy109: 529–541.
15.
LangeMFockenU (2006) Physical Approach to Short-Term Wind Power Prediction. New York: Springer Berlin Heidelberg.
16.
LinK-PPaiP-FTingY-J (2019) Deep belief networks with genetic algorithms in forecasting wind speed. IEEE Access7: 99244–99253.
17.
LiuYGuanLHouC, et al. (2019) Wind power short-term prediction based on LSTM and discrete wavelet transform. Applied Sciences9(6): 1108.
18.
LiYShiHHanF, et al. (2019) Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy. Renewable Energy135: 540–553.
19.
LoweryCO’MalleyM (2012) Impact of wind forecast error statistics upon Unit Commitment. IEEE Transactions on Sustainable Energy3(4): 760–768.
20.
LuoXSunJWangL, et al. (2018) Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy. IEEE Transactions on Industrial Informatics14(11): 4963–4971.
21.
McCullochWSPittsW (1943) A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics5(4): 115–133.
22.
PascanuRMikolovTBengioY (2013) On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning2: 1310–1318.
RenCAnNWangJ, et al. (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems56: 226–239.
25.
Santamaría-BonfilGReyes-BallesterosAGershensonC (2016) Wind speed forecasting for wind farms: A method based on support vector regression. Renewable Energy85: 790–809.
26.
SomanSSZareipourHMalikO, et al. (2010) A review of wind power and wind speed forecasting methods with different time horizons in North American. In: North American Power Symposium (NAPS)2010, Arlington, TX, USA, 26–28 September, pp.1–8. New York, NY: IEEE.
27.
SyuY-DWangJ-CChouC-Y, et al. (2020) Ultra-Short-Term Wind Speed Forecasting for Wind Power Based on Gated Recurrent Unit. In: 2020 8th International Electrical Engineering Congress (iEECON), Chiang Mai, Thailand, 04-06 March, pp.1-4. New York, NY: IEEE. doi: 10.1109/iEECON48109.2020.229518.
28.
TasnimSRahmanAOoAMT, et al. (2017) Autoencoder for wind power prediction. Renewables Wind Water and Solar4(1): 6.
29.
TewariSGeyerCJMohanN (2011) A statistical model for wind power forecast error and its application to the estimation of penalties in liberalized markets. IEEE Transactions on Power Systems26(4): 2031–2039.
30.
TomicSD (2013) A Study of the Impact of Load Forecasting Errors on Trading and Balancing in a Microgrid. In: 2013 IEEE Green Technologies Conference (GreenTech), Denver, CO, USA, 04–05 April, pp.443–450. New York, NY: IEEE. 443–450. doi: 10.1109/GreenTech.2013.74.
31.
WangJSongYLiuF, et al. (2016) Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models. Renewable and Sustainable Energy Reviews60: 960–981.
32.
WangKQiXLiuH, et al. (2018) Deep belief network based k-means cluster approach for short-term wind power forecasting. Energy165: 840–852.
33.
WuYKHongJS (2007) A literature review of wind forecasting technology in the world. IEEE Lausanne Power Technology1(5): 504–509.
34.
ZhangCWeiHZhaoJ, et al. (2016) Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renewable Energy96: 727–737.
35.
ZhangHChenLQuY, et al. (2014) Support vector regression based on grid-search method for short-term wind power forecasting. Journal Applied Mathematics2014: 1–11.
36.
ZhengWPengXLuD, et al. (2017) Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach. Energy Conversion and Management151: 737–752.
37.
ZickfeldKSolomonSGilfordDM (2017) Centuries of thermal sea-level rise due to anthropogenic emissions of short-lived greenhouse gases. Proceedings of the National Academy of Sciences114(4): 657–662.
38.
ZielFCroonenbroeckCAmbachD (2016) Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity. Applied Energy177: 285–297.
