Autonomous Kernel Based Models for Short-Term Load Forecasting
Autonomous Kernel Based Models for Short-Term Load Forecasting
摘要
The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.
参考文献24
-
1R. Ramanathan, R. Engle, C.W.J. Granger, F. Vahid-Araghi, C. Brace, Short-run fore-casts of electricity loads and peaks, InternationalJournal of Forecasting 13 (2) (1997) 161-174.
-
2G.A. Darbellay, M. Slama, Forecasting the short-term demand for electricity: Do neural networks stand a better chance, InternationalJournal of Forecasting 16 (I) (2000) 71-83.
-
3S.J. Huang, K.R. Shih, Short-term load forecasting via ARMA model identification including non-gaussian process considerations, IEEE Transactions on Power Systems 18 (2) (2003) 673-679.
-
4A. Khotanzad, R.R. Afkharni, D. Maratukulam, ANNSTLF-Artificial neural network short-term load forecaster generation three, IEEE Transactions on Power Systems 13 (4) (1998) 1413-1422.
-
5H.S. Hippert, R.C. Souza, C.E. Pedreira, Neural networks for load forecasting: A review and evaluation, IEEE Transactions on Power Systems 16 (1) (2001) 44-55.
-
6O.A.S. Carpinteiro, AJ.R. Reis, A.P. Alves da Silva, A hierarchical neural model in short-term load forecasting, Applied Soft Computing 4 (4) (2004) 405-412.
-
7H. Mori, H. Kobayashi, Optimal fuzzy inference for short-term load forecasting, IEEE Transactions on Power Systems II (I) (1996) 390-396.
-
8T. Senjyu, S. Higa, K. Uezato, Future load curve shaping based on similarity using fuzzy logic approach, lEE Proceedings on Generation, Transmission and Distribution 145 (4) (1998) 375-380.
-
9P.A. Mastorocostas, lB. Theocharis, A.G. Bakirtzis, Fuzzy modeling for short term load forecasting using the orthogonal least squares method, IEEE Transactions on Power Systems 14 (I) (1999) 29-36.
-
10M. Tarnirni, R. Egbert, Short term electric load forecasting via fuzzy neural collaboration, Electric Power Systems Research 56 (3) (2000) 243-248.
-
1田丽,黄世伟,李泽应,王军.基于PSO的RBF神经网络短期电力负荷预测[J].安徽工程科技学院学报（自然科学版）,2007,22(2):33-35. 被引量：1
-
2宋云,陈焕文,张晓华.一种整合多种计算方法的短期负荷预测模型[J].计算机工程与应用,2007,43(3):185-188. 被引量：1
-
3王刚,刘大勇.评估系统在短期负荷预测模型中的应用[J].沈阳工程学院学报（自然科学版）,2007,3(3):260-262.
-
4李龙煊.基于BP网络的短期负荷预测模型[J].电工技术,2001(7):16-17. 被引量：1
-
5DONG Liang,MU Zhichun (Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China).Short-term load forecasting based on fuzzy neural network[J].International Journal of Minerals,Metallurgy and Materials,1997,11(3):46-48.
-
6岑文辉,雷友坤,谢恒.应用人工神经网与遗传算法进行短期负荷预测[J].电力系统自动化,1997,21(3):29-32. 被引量：23
-
7杨廷志,文小飞,万俊,李书.改进神经网络的短期负荷预测模型及仿真[J].计算机仿真,2014,31(10):145-150. 被引量：9
-
8李斌.电力系统潮流误差反向传播算法[J].青海大学学报（自然科学版）,2005,23(6):9-11.
-
9邓培敏,陈明华,佘恬.Elman网络在短期负荷预测中的应用[J].企业科技与发展（下半月）,2009(2):27-29. 被引量：3
-
10尤勇,盛万兴,王孙安.基于人工免疫网络的短期负荷预测模型[J].中国电机工程学报,2003,23(3):26-29. 被引量：30
