1Li L L, Gong C Z, Wang D Y, et al. Multi-agent simulation of the time-of-use pricing policy in an urban natural gas pipeline network: A case study of Zhengzhou[J]. Energy, 2013, 52(1): 37-43.
2Lingras P, Butz C J. Rough support vector regression[J]. European Journal of Operational Research, 2010, 206(2): 445-455.
3Javed F, Arshad N, Wallin F, et al. Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting[J]. Applied Energy, 2012, 96: 150- 160.
4Sudheer C, Anand N, Panigrahi B K, et al. Streamflow forecasting by SVM with quantum behaved particle swarm optimization[J]. Neurocomputing, 2013, 101:18- 23.
6Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293 -300.
7Mohandes M. Support vector machines for short-term electrical load forecasting[J]. International Journal of Energy Research, 2002, 26(4): 335- 345.
8Nagi J, Yap K S, Nagi F, et al. A computational intelligence scheme for the prediction of the daily peak load[J]. Applied Soft Computing, 2011, 11(8): 4773-4788.
9Sulaiman M H, Mustafa M W, Shareef H, et al. An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system[J]. International Journal of Electrical Power & Energy Systems, 2012, 37(1): 67-77.
10Deihimi A, Showkati H. Application of echo state networks in short-term electric load forecasting[J]. Energy, 2012, 39(1): 327-340.