A novel hybrid short term load forecasting model considering the error of numerical weather prediction

Energies

Volumn 9, Issue 12, 2016, Pages

  Cai, Guowei ^a   Wang, Wenjin ^a   Lu, Junhai ^b  

^a NORTHEAST ELECTRIC POWER UNIVERSITY   (China)

^b STATE GRID LIAONING ELECTRIC POWER SUPPLY CO LTD   (China)

Author keywords

Artificial bee colony (ABC); Seasonal autoregressive integrated moving average (SARIMA); Short term load forecasting (STLF); Support vector regression (SVR)

Indexed keywords

ELECTRIC POWER PLANT LOADS; ERRORS; NUMERICAL MODELS; OPTIMIZATION;

ARTIFICIAL BEE COLONIES (ABC); ARTIFICIAL BEE COLONY ALGORITHMS (ABC); INTERNATIONAL ORGANIZATION FOR STANDARDIZATIONS; LOAD FORECASTING MODEL; NUMERICAL WEATHER PREDICTION; SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGES; SHORT TERM LOAD FORECASTING; SUPPORT VECTOR REGRESSION (SVR);

WEATHER FORECASTING;

EID: 85002551432     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en9120994     Document Type: Article

References (41)

1. Raza, M.Q.; Khosravi, A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 2015, 50, 1352-1372. [CrossRef]
2. Bozic, M.; Stojanovic, M.; Stajic, Z.; Floranovic, N. Mutual information-based inputs selection for electric load time series forecasting. Entropy 2013, 15, 926-942. [CrossRef]
3. Kouhi, S.; Keynia, F. A new cascade NN based method to short-term load forecast in deregulated electricity market. Energy Convers. Manag. 2013, 71, 76-83. [CrossRef]
4. Kulkarni, S.; Simon, S.P.; Sundareswaran, K. A spiking neural network (SNN) forecast engine for short-term electrical load forecasting. Appl. Soft Comput. 2013, 13, 3628-3635. [CrossRef]
5. Li, S.; Wang, P.; Goel, L. Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr. Power Syst. Res. 2015, 122, 96-103. [CrossRef]
6. Reis, A.J.R.; da Silva, A.P.A. Feature extraction via multiresolution analysis for short-term load forecasting. IEEE Trans. Power Syst. 2005, 20, 189-198.
7. Sudheer, G.; Suseelatha, A. Short term load forecasting using wavelet transform combined with Holt-Winters and weighted nearest neighbor models. Int. J. Electr. Power 2015, 64, 340-346. [CrossRef]
8. Wang, J.J.;Wang, J.Z.; Li, Y.N.; Zhu, S.L.; Zhao, J. Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. Int. J. Electr. Power 2014, 62, 816-824. [CrossRef]
9. Li, S.;Wang, P.; Goel, L. A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. IEEE Trans. Power Syst. 2016, 31, 1788-1798. [CrossRef]
10. Gu, C.J.; Yang, D.Z.; Jirutitijaroen, P.;Walsh,W.M.; Reindl, T. Spatial load forecasting with communication failure using time-forward kriging. IEEE Trans. Power Syst. 2014, 29, 2875-2882.
11. Lee, W.J.; Hong, J. A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Int. J. Electr. Power 2015, 64, 1057-1062. [CrossRef]
12. Niu, D.X.; Shi, H.F.;Wu, D.D. Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm. Appl. Soft Comput. 2012, 12, 1822-1827. [CrossRef]
13. Hooshmand, R.A.; Amooshahi, H.; Parastegari, M. A hybrid intelligent algorithm based short-term load forecasting approach. Int. J. Electr. Power 2013, 45, 313-324. [CrossRef]
14. 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 2015, 67, 230-237. [CrossRef]
15. Kouhi, S.; Keynia, F.; Ravadanegh, S.N. A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection. Int. J. Electr. Power 2014, 62, 862-867. [CrossRef]
16. Ko, C.N.; Lee, C.M. Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 2013, 49, 413-422. [CrossRef]
17. Ceperic, E.; Ceperic, V.; Baric, A. A strategy for short-term load forecasting by support vector regression machines. IEEE Trans. Power Syst. 2013, 28, 4356-4364. [CrossRef]
18. 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]
19. Che, J.X.; Wang, J.Z. Short-term load forecasting using a kernel-based support vector regression combination model. Appl. Energy 2014, 132, 602-609. [CrossRef]
20. Hu, Z.Y.; Bao, Y.K.; Xiong, T. Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl. Soft Comput. 2014, 25, 15-25. [CrossRef]
21. Lin, C.T.; Chou, L.D.; Chen, Y.M.; Tseng, L.M. A hybrid economic indices based short-term load forecasting system. Int. J. Electr. Power 2014, 54, 293-305. [CrossRef]
22. Moazzami, M.; Khodabakhshian, A.; Hooshmand, R. A new hybrid day-ahead peak load forecasting method for Iran's National Grid. Appl. Energy 2013, 101, 489-501. [CrossRef]
23. Wang, J.J.; Li, L.; Niu, D.X.; Tan, Z.F. An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl. Energy 2012, 94, 65-70. [CrossRef]
24. Quan, H.; Srinivasan, D.; Khosravi, A. Uncertainty handling using neural network-based prediction intervals for electrical load forecasting. Energy 2014, 73, 916-925. [CrossRef]
25. Che, J.X. A novel hybrid model for bi-objective short-term electric load forecasting. Int. J. Electr. Power 2014, 61, 259-266. [CrossRef]
26. Hong, W.C.; Dong, Y.C.; Zhang, W.Y.; Chen, L.Y.; Panigrahi, B.K. Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. Int. J. Electr. Power 2013, 44, 604-614. [CrossRef]
27. Chen, Y.H.; Yang, Y.; Liu, C.Q.; Li, C.H.; Li, L. A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting. Appl. Math. Model. 2015, 39, 2617-2632. [CrossRef]
28. Yan, X.; Chowdhury, N.A. Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input. Int. J. Electr. Power 2014, 63, 64-70. [CrossRef]
29. Zhang,W.Y.; Hong,W.C.; Dong, Y.C.; Tsai, G.; Sung, J.T.; Fan, G.F. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy 2012, 45, 850-858. [CrossRef]
30. Hong, W.C. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers. Manag. 2009, 50, 105-117. [CrossRef]
31. Yang, D.; Liu, Y.; Li, S.; Li, X.; Ma, L. Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mech. Mach. Theory 2015, 90, 219-229. [CrossRef]
32. Mandal, S.K.; Chan, F.T.S.; Tiwari, M.K. Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM. Expert Syst. Appl. 2012, 39, 3071-3080. [CrossRef]
33. Kang, F.; Li, J. Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J. Comput. Civ. Eng. 2015, 30. [CrossRef]
34. Karaboga, D.; Ozturk, C.; Karaboga, N.; Gorkemli, B. Artificial bee colony programming for symbolic regression. Inf. Sci. 2012, 209, 1-15. [CrossRef]
35. Mernik, M.; Liu, S.H.; Karaboga, D.; Crepinsek, M. On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation. Inf. Sci. 2015, 291, 115-127. [CrossRef]
36. El-Fergany, A.A.; Abdelaziz, A.Y. Capacitor placement for net saving maximization and system stability enhancement in distribution networks using artificial bee colony-based approach. Int. J. Electr. Power 2014, 54, 235-243. [CrossRef]
37. Karaboga, D.; Gorkemli, B.; Ozturk, C.; Karaboga, N. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 2014, 42, 21-57. [CrossRef]
38. Areekul, P.; Senjyu, T.; Toyama, H.; Yona, A. Notice of violation of IEEE publication principles a hybrid ARIMA and neural network model for short-term price forecasting in deregulated market. IEEE Trans. Power Syst. 2010, 25, 524-530. [CrossRef]
39. Hu, Z.Y.; Bao, Y.K.; Xiong, T.; Chiong, R. Hybrid filter-wrapper feature selection for short-term load forecasting. Eng. Appl. Artif. Intell. 2015, 40, 17-27. [CrossRef]
40. Ding, N.; Benoit, C.; Foggia, G.; Besanger, Y.; Wurtz, F. Neural network-based model design for short-term load forecast in distribution systems. IEEE Trans. Power Syst. 2016, 31, 72-81. [CrossRef]
41. Che, J.X.;Wang, J.Z.; Tang, Y.J. Optimal training subset in a support vector regression electric load forecasting model. Appl. Soft Comput. 2012, 12, 1523-1531. [CrossRef]