A hybrid method of clipping and artificial neural network for electricity price zone forecasting

被引：0

作者：

Mori, H. ^{[1
]}

Awata, A. ^{[1
]}

机构：

[1] Meiji Univ, Dept Elect & Elect Engn, Tama Ku, 1-1-1 Higashimita, Kawasaki, Kanagawa 2148571, Japan

来源：

2006 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS, VOLS 1 AND 2 | 2006年

关键词：

forecasting; prediction method; data mining; time series; neural network applications; MLP; intelligent systems;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a new method for electricity price zone forecasting. The proposed method makes use of the clipping technique that is one of data mining techniques for simplifying the relationship between input and output variables. It expresses an output variable in binary number. Electricity price forecasting is difficult to handle due to the nonlinearity of time series. This paper predicts the one-step-ahead price zone. In this paper, the normalized radial basis function network is used as an artificial neural network (ANN) to evaluate the predicted price. The proposed method is tested for the electricity price in the New England power market.

引用

页码：275 / 280

页数：6

共 50 条

[31] Neural Network Based Model Comparison for Intraday Electricity Price Forecasting
Oksuz, Ilkay
Ugurlu, Umut
[J]. ENERGIES, 2019, 12 (23)
[32] Electricity price forecasting of deregulated market using Elman neural network
Vardhan, N. Harsha
Chintham, Venkaiah
[J]. 2015 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2015,
[33] Electricity Price Forecasting using Asymmetric Fuzzy Neural Network Systems
Alshejari, Abeer
Kodogiannis, Vassilis S.
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2017,
[34] Electricity price short-term forecasting using artificial neural networks
Applied Computing Research Institute, La Trobe University, Melbourne, Vic., Australia
[J]. IEEE Trans Power Syst, 3 (851-857):
[35] Electricity price short-term forecasting using artificial neural networks
Szkuta, BR
Sanabria, LA
Dillon, TS
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1999, 14 (03) : 851 - 857
[36] A hybrid ARFIMA and neural network model for electricity price prediction
Chaabane, Najeh
[J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 55 : 187 - 194
[37] Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation
Nascimento, Joao
Pinto, Tiago
Vale, Zita
[J]. 2019 IEEE MILAN POWERTECH, 2019,
[38] Rainfall forecasting of typical coastal zone with artificial neural network
Zheng, Wei
Shi, Honghua
Gao, Huiwang
Ding, Dewen
Wang, Xuewu
Chen, Shang
[J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2007, 14 : 863 - 869
[39] Day-ahead electricity price forecasting by a new hybrid method
Zhang, Jinliang
Tan, Zhongfu
Yang, Shuxia
[J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2012, 63 (03) : 695 - 701
[40] Building a hybrid neural network model for gold price forecasting
Matroushi, M. S.
Samarasinghe, S.
[J]. 18TH WORLD IMACS CONGRESS AND MODSIM09 INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION: INTERFACING MODELLING AND SIMULATION WITH MATHEMATICAL AND COMPUTATIONAL SCIENCES, 2009, : 1544 - 1544

← 1 2 3 4 5 →