A study on the application of BPNN based on Minimum Error Entropy in electricity price forecasting

Author

Zhang, Jianhua ; Wang, Fang ; Wang, Rui ; Hou, Guolian

Author_Institution

Beijing Key Lab. of Ind. Process Meas. & Control New Technol. & Syst., North China Electr. Power Univ., Beijing, China

fYear

2011

fDate

21-23 June 2011

Firstpage

925

Lastpage

929

Abstract

Market clearing price (MCP) forecasting techniques is very important for the development of the electricity market. A three-layered neural network is used to predict electricity prices. MCP is seen as a multi-input single-output system and the historical electricity price and load data is utilized in an electricity market. The neural network is based on Minimum Entropy Error (MEE) cost function and Batch-Sequential mode. Compared with other models, the proposed approach improves the prediction accuracy and speed.

Keywords

backpropagation; costing; load forecasting; neural nets; power engineering computing; power markets; BPNN; MCP forecasting techniques; batch-sequential mode; cost function; electricity market; electricity price forecasting; market clearing price; minimum error entropy; multiinput singleoutput system; three-layered neural network; Accuracy; Artificial neural networks; Electricity; Entropy; Forecasting; Predictive models; Training; batch-sequential mode; electricity price forecasting; entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on

Conference_Location

Beijing

ISSN

pending

Print_ISBN

978-1-4244-8754-7

Electronic_ISBN

pending

Type

conf

DOI

10.1109/ICIEA.2011.5975719

Filename

5975719