Predictions of System Marginal Price of Electricity Using Recurrent Neural Network

Author

Lin, Zhiling ; Gao, Liqun ; Zhang, Dapeng

Author_Institution

Coll. of Inf. Sci. & Eng., Northeast Univ., Shenyang

Volume

2

fYear

0

fDate

0-0 0

Firstpage

7592

Lastpage

7595

Abstract

The accuracy of system marginal price (SMP) is important for bidding of generation companies. Based on analyzing characteristic of SMP, electrical load, historical value of SMP corresponding time and tendency of current SMP are regarded as three main influencing factors in estimating the next numeric value of SMP. A recurrent neural network is also introduced to forecast the SMP, because it has an ability of mapping dynamic system and SMP is regarded as a result of dynamic power market run. Aiming at the difficulty of determining neural network´s structure and weights, the GA optimization algorithm is used to get them by previously combining binary encoding and real encoding. The history data of American California showed this method is effective and the forecast model is accurate

Keywords

electricity supply industry; genetic algorithms; load forecasting; power generation economics; power markets; recurrent neural nets; binary encoding; electrical load; genetic algorithm; mapping dynamic system; power generation companies; real encoding; recurrent neural network; system marginal price; Artificial neural networks; Economic forecasting; Educational institutions; Electricity supply industry; Encoding; Power generation; Power markets; Recurrent neural networks; Supply and demand; Weather forecasting; Forecasting; Genetic algorithm; Recurrent neural network; System marginal price;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713442

Filename

1713442