Correction: Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by improved cuckoo search (Energies (2016) 9 (827) DOI: 10.3390/en9100827);Short-term load forecasting based on wavelet transform and least squares support vector machine optimized by improved cuckoo search

Energies

Volumn 9, Issue 10, 2016, Pages

  Liang, Yi ^a   Niu, Dongxiao ^a   Ye, Minquan ^b   Hong, Wei Chiang ^c  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^c ORIENTAL INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Cuckoo search; Gauss disturbance; Least squares support vector machine; Short term load forecasting; Wavelet transform

Indexed keywords

DEREGULATION; ELECTRIC POWER PLANT LOADS; FORECASTING; OPTIMIZATION; SUPPORT VECTOR MACHINES; VECTORS;

CUCKOO SEARCHES; ELECTRICAL LOAD FORECASTING; GAUSS DISTURBANCE; HIGH FREQUENCY COMPONENTS; LEAST SQUARES SUPPORT VECTOR MACHINES; SHORT TERM ELECTRIC LOAD; SHORT TERM LOAD FORECASTING; SHORT-TERM FORECASTING;

WAVELET TRANSFORMS;

EID: 85021184170     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en9121076     Document Type: Erratum

References (40)

1. Bozic,M.; Stojanovic,M.; Stajic, Z.; Tasić, D. A newtwo-stage approach to short termelectrical load forecasting. Energies 2013, 6, 2130-2148. [CrossRef]
2. Liang, R.H.; Chen, Y.K.; Chen, Y.T. Volt/Var control in a distribution system by a fuzzy optimization approach. Int. J. Electr. Power Energy Syst. 2011, 33, 278-287. [CrossRef]
3. Niknam, T.; Zare, M.; Aghaei, J. Scenario-based multiobjective Volt/Var control in distribution networks including renewable energy sources. IEEE Trans. Power Deliv. 2012, 27, 2004-2019. [CrossRef]
4. Lee, C.M.; Ko, C.N. Short-term load forecasting using lifting scheme and ARIMA models. Expert Syst. Appl. 2011, 38, 5902-5911. [CrossRef]
5. Pappas, S.S.; Ekonomou, L.; Karamousantas, D.C.; Chatzarakis, G.E.; Katsikas, S.K.; Liatsis, P. Electricity demand loads modeling using Auto RegressiveMoving Average (ARMA) models. Energy 2008, 33, 1353-1360. [CrossRef]
6. Pappas, S.S.; Ekonomou, L.; Karampelas, P.; Karamousantasd, D.C.; Katsikase, S.K.; Chatzarakis, G.E.; Skafidas, P.D. Electricity demand load forecasting of the Hellenic power system using an ARMA model. Electr. Power Syst. Res. 2010, 80, 256-264. [CrossRef]
7. Kandil, N.; Wamkeue, R.; Saad, M.; Georges, S. An efficient approach for short term load forecasting using artificial neural networks. Int. J. Electr. Power Energy Syst. 2006, 28, 525-530. [CrossRef]
8. Yu, F.; Xu, X. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl. Energy 2014, 134, 102-113. [CrossRef]
9. Chaturvedi, D.K.; Sinha, A.P.; Malik, O.P. Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Int. J. Electr. Power Energy Syst. 2015, 67, 230-237. [CrossRef]
10. Hernandez, L.; Baladrón, C.; Aguiar, J.M.; Carro, B.; Sanchez-Esguevillas, A.J.; Lloret, J. Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies 2013, 6, 1385-1408. [CrossRef]
11. Amjady, N.; Keynia, F. A new neural network approach to short term load forecasting of electrical power systems. Energies 2011, 4, 488-503. [CrossRef]
12. Pan, D.; Xie, K.; Guo, T.; Huang, X. Short-term load forecasting for electric power systems using the PSO-SVR and FCM clustering techniques. Energies 2011, 4, 173-184.
13. Kavousi-Fard, A.; Samet, H.; Marzbani, F. A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting. Expert Syst. Appl. 2014, 41, 6047-6056. [CrossRef]
14. Vapnik, V. The Nature of Statistical Learning Theory; Springer Science & Business Media: Berlin, Germany, 2000.
15. Sun, W.; Liang, Y. Least-Squares Support Vector Machine Based on Improved Imperialist Competitive Algorithm in a Short-Term Load Forecasting Model. J. Energy Eng. 2014, 141, 04014037. [CrossRef]
16. Mesbah, M.; Soroush, E.; Azari, V.; Lee, M.; Bahadori, A.; Habibnia, S. Vapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm. J. Supercrit. Fluids 2015, 97, 256-267. [CrossRef]
17. Liu, H.; Yao, X.; Zhang, R.; Liu, M.; Hu, Z.; Fan, B. Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. J. Phys. Chem. B 2005, 109, 20565-20571. [CrossRef] [PubMed]
18. Sun, W.; Liang, Y. Research of least squares support vector regression based on differential evolution algorithm in short-term load forecasting model. J. Renew. Sustain. Energy 2014, 6, 053137. [CrossRef]
19. Sun, W.; Liu, M.H.; Liang, Y. Wind speed forecasting based on FEEMD and LSSVM optimized by the bat algorithm. Energies 2015, 8, 6585-6607. [CrossRef]
20. Sun,W.; Liang, Y. Comprehensive evaluation of cleaner production in thermal power plants using particle swarm optimization based least squares support vector machines. J. Inf. Comput. Sci. 2015, 12, 1993-2000. [CrossRef]
21. Sun,W.; Liang, Y.; Xu, Y.F. Application of carbon emissions prediction using least squares support vector machine based on grid search. WSEAS Trans. Syst. Control 2015, 10, 95-104.
22. Gorjaei, R.G.; Songolzadeh, R.; Torkaman, M.; Safari, M.; Zargar, G. A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes. J. Nat. Gas Sci. Eng. 2015, 24, 228-237. [CrossRef]
23. Liu, D.; Niu, D.; Wang, H.; Fan, L. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew. Energy 2014, 62, 592-597. [CrossRef]
24. Yang, X.S.; Deb, S. Cuckoo Search via Levy Flights. In Proceedings of the World Congress on IEEE 2010, Barcelona, Spain, 18-23 July 2010.
25. Valian, E.; Tavakoli, S.; Mohanna, S.; Haghi, A. Improved cuckoo search for reliability optimization problems. Comput. Ind. Eng. 2013, 64, 459-468. [CrossRef]
26. Wong, P.K.;Wong, K.I.; Chi, M.V.; Cheung, C.S. Modelling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew. Energy 2015, 74, 640-647. [CrossRef]
27. Abdelaziz, A.Y.; Ali, E.S. Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system. Int. J. Electr. Power Energy Syst. 2015, 73, 632-643. [CrossRef]
28. Wang, J.; Jiang, H.; Wu, Y.; Dong, Y. Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm. Energy 2015, 81, 627-644. [CrossRef]
29. Deihimi, A.; Orang, O.; Showkati, H. Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 2013, 57, 382-401. [CrossRef]
30. Abdoos, A.; Hemmati, M.; Abdoos, A.A. Short term load forecasting using a hybrid intelligent method. Knowl.-Based Syst. 2015, 76, 139-147. [CrossRef]
31. Chen, J.; Li, Z.; Pan, J.; Chen, G.; Zi, Y.; Yuan, J.; Chen, B.; He, Z. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 2016, 70, 1-35. [CrossRef]
32. Zhang, Y.; Li, H.; Wang, Z.; Li, J. A preliminary study on time series forecast of fair-weather atmospheric electric field with WT-LSSVM method. J. Electrost. 2015, 75, 85-89. [CrossRef]
33. Shayeghi, H.; Ghasemi, A. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Convers. Manag. 2013, 74, 482-491. [CrossRef]
34. Suykens, J.A.K.; Vandewalle, J. Least squares support vector machine classifiers. Neural Process. Lett. 1999, 9, 293-300. [CrossRef]
35. Sun, W.; Liang, Y. Least-squares support vector machine based on improved imperialist competitive algorithm in a short-term load forecasting model. J. Energy Eng. 2015, 141, 04014037. [CrossRef]
36. Yang, X.S.; Deb, S. Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 2010, 1, 330-343. [CrossRef]
37. Hooshmand, R.A.; Amooshahi, H.; Parastegari, M. A hybrid intelligent algorithm based short-term load forecasting approach. Int. J. Electr. Power Energy Syst. 2013, 45, 313-324. [CrossRef]
38. Bahrami, S.; Hooshmand, R.A.; Parastegari, M. Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy 2014, 72, 434-442. [CrossRef]
39. Hyndman, R.J.; Koehler, A.B. Another look at measures of forecast accuracy. Int. J. Forecast. 2006, 22, 679-688. [CrossRef]
40. Niu, D.; Wang, Y.; Wu, D.D. Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 2010, 37, 2531-2539. [CrossRef]