Wind power forecasting approach using neuro-fuzzy system combined with wavelet packet decomposition,data preprocessing,and forecast combination framework
Restricted accessResearch articleFirst published online August, 2017
Wind power forecasting approach using neuro-fuzzy system combined with wavelet packet decomposition,data preprocessing,and forecast combination framework
Wind power forecasting is presently one of the challenging tasks to deal with supply-demand balance in modern electric power systems. Accurate wind power predictions are needed to reduce the risk in uncertainty and enable for better dispatch, scheduling, and power system integration. This article deals with the challenge of wind power forecasting by proposing the application of the forecasting methodology using the wavelet packet decomposition principles, neuro-fuzzy systems, as well as the benefits of data preprocessing and forecast combination framework. The used data consist of the quarter-hourly observations of wind power generation in France, and the proposed method is used to ensure forecasts for a time horizon of an hour-ahead. The obtained results indicate the superior accuracy of the proposed model with an average mean absolute percentage error of 3.408%, which means there is possibility to construct a high-precision method using only the historical wind power data.
AhlstromMLJonesLZavadilR. (2005) The future of wind forecasting and utility operations. IEEE Power and Energy Magazine3(6): 57–64.
2.
BuhanSTerciyanliEOzkanMB. Verification of a very short term wind power forecasting algorithm for Turkish transmission grid. In: Proceedings of the 2013 fourth international conference on power engineering, energy and electrical drives (POWERENG), Istanbul, Turkey, 13–17 May 2013, pp. 1217–1221. New York: IEEE.
3.
CaoYLiuYZhangD. (2013) Wind power ultra-short-term forecasting method combined with pattern-matching and ARMA-model. In: Proceedings of the 2013 IEEE Grenoble PowerTech, Grenoble, 16–20 June 2013, pp. 1–4. New York: IEEE.
4.
CatalãoJPSPousinhoHMIMendesVMF (2011) Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renewable Energy36(4): 1245–1251.
5.
CheJXWangJZ (2014) Short-term load forecasting using a kernel-based support vector regression combination model. Applied Energy132: 602–609.
6.
ColakISagirogluSYesilbudakM (2012) Data mining and wind power prediction: A literature review. Renewable Energy46: 241–247.
7.
De AquinoRRBLudermirTBNobrega NetoO. (2013) Forecasting models of wind power in Northeastern of Brazil. In: Proceedings of the 2013 international joint conference on neural networks (IJCNN), Dallas, TX, 4–9 August 2013, pp. 1–8. New York: IEEE.
8.
GiebelGLandbergLKariniotakisG. State-of-the-art on methods and software tools for short-term prediction of wind energy production. In: Proceedings of the European wind energy conference and exhibition (EWEC), Madrid, 16–19 June 2003.
9.
GuoJZhangXYJangW. (2016) An improved wind power forecasting model based on eliminating outliers. Wind Engineering40(1): 50–58.
10.
HongYYWuCP (2010) Hour-ahead wind power and speed forecasting using market basket analysis and radial basis function network. In: Proceedings of the 2010 international conference on power system technology (POWERCON), Zhejiang, China, 24–28 October 2010, pp. 1–6. New York: IEEE.
11.
HongYYChangHLChiuCS (2010) Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs. Energy35(9): 3870–3876.
12.
HuangDZGongRXGongS (2015) Prediction of wind power by chaos and BP artificial neural networks approach based on genetic algorithm. Journal of Electrical Engineering & Technology10(1): 41–46.
13.
JiangYSongZKusiakA (2013) Very short-term wind speed forecasting with Bayesian structural break model. Renewable Energy50: 637–647.
14.
JohnsonPNegnevitskyMMuttaqiKM (2007) Short term wind power forecasting using adaptive neuro-fuzzy inference systems. In: Proceedings of the Australasian universities power engineering conference (AUPEC 2007), Perth, WA, Australia, 9–12 December 2007, pp. 1–6. New York: IEEE.
LangeMFockenUHeinemannD (2002) PREVIENTO: Regional wind power prediction with risk control. In: Proceedings of the world wind energy conference and exhibition, Berlin, 2–6 July 2002.
17.
LaouafiAMordjaouiMDibD (2015a) One-hour ahead electric load and wind-solar power generation forecasting using artificial neural network. In: Proceedings of the 6th international renewable energy congress (IREC), Sousse, Tunisia, 24–26 March 2015, pp. 1–6. New York: IEEE.
18.
LaouafiAMordjaouiMDibD (2015b) One-hour ahead electric load forecasting using neuro-fuzzy system in a parallel approach. In: AzarAHVaidyanathanS (eds) Computational Intelligence Applications in Modeling and Control (Studies in computational intelligence vol. 575), pp. 95–121. Berlin: Springer.
19.
LaouafiAMordjaouiMLaouafiF. (2016) Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology. International Journal of Electrical Power & Energy Systems77: 136–144.
20.
LiFWeiY (2012) A probability-driven multilayer framework for scheduling intermittent renewable energy. IEEE Transactions on Sustainable Energy3(3): 455–464.
21.
LiuHTianHGChenC. (2010) A hybrid statistical method to predict wind speed and wind power. Renewable Energy35(8): 1857–1861.
22.
MishraSSharmaAPandaG (2011) Wind power forecasting model using complex wavelet theory. In: Proceedings of the international conference on energy, automation, and signal (ICEAS), Bhubaneswar, India, 28–30 December 2011, pp. 1–4. New York: IEEE.
23.
ParamasivanSKLopezD (2016) Forecasting of wind speed using feature selection and neural networks. International Journal of Renewable Energy Research6(3): 833–837.
24.
PotterCWNegnevitskyM (2006) Very short-term wind forecasting for Tasmanian power generation. IEEE Transactions on Power Systems21(2): 965–972.
25.
RahmaniRYusofRSeyedmahmoudianM. (2013) Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting. Journal of Wind Engineering and Industrial Aerodynamics123(Pt A): 163–170.
26.
ShiJLeeWJLiuY. (2011) Short term wind power forecasting using Hilbert-Huang transform and artificial neural network. In: Proceedings of the 2011 4th international conference on electric utility deregulation and restructuring and power technologies (DRPT), Weihai, China, 6–9 July 2011, pp. 162–167. New York: IEEE.
27.
TouaniIYuWGagnonY. (2013) Development of a wind to power model for wind farm power production forecasting. Wind Engineering37(4): 347–368.
28.
WangXGuoPHuangX (2011) A review of wind power forecasting models. Energy Procedia12: 770–778.
29.
WuYKLinCLKeBR. (2010) Development of an industrial forecasting tool in wind power. In: Proceedings of the 2010 5th IEEE conference on industrial electronics and applications (ICIEA), Taichung, Taiwan, 15–17 June 2010, pp. 614–619. New York: IEEE.
30.
XiaJZhaoPDaiY (2010) Neuro-fuzzy networks for short-term wind power forecasting. In: Proceedings of the 2010 international conference on power system technology (POWERCON), Zhejiang, China, 24–28 October 2010, pp. 1–5. New York: IEEE.
31.
YuWPlanteADyckS. (2014) An operational application of NWP models in a wind power forecasting demonstration experiment. Wind Engineering38(1): 1–21.
32.
ZengJQiaoW (2011) Support vector machine-based short-term wind power forecasting. In: Proceedings of the 2011 IEEE/PES power systems conference and exposition (PSCE), Phoenix, AZ, 20–23 March 2011, pp. 1–8. New York: IEEE.
