Forecast of system marginal price by a neural network with critic mechanism

Author

Zhi-ling, Lin ; Ze-fang, Jia ; Hong-jun, Wang ; You-jun, Yue ; En-zeng, Dong

Author_Institution

Sch. of Electr. Eng. & Autom., Tianjin Univ. of Technol., Tianjin, China

fYear

2010

Firstpage

2361

Lastpage

2365

Abstract

A RBF neural network considering the critic mechanism is introduced to predict the system marginal price (SMP). The system consists of three elements, which are a predictor, an evaluator and a learning machine. The predictor is used to forecast the future SMP. The estimator is used to evaluate the prediction´s validity. The explorer is used to determine the predictive step length. And the learning machine is used to keep the predictor self-learning. So the predictor can conform to SMP by self-learning and be in a good forecasting state. The simulation shows the proposed method has higher forecasting accuracy in irregular SMP cases than the conventional method has.

Keywords

forecasting theory; learning (artificial intelligence); pricing; radial basis function networks; RBF neural network; SMP; critic mechanism; learning machine; neural network; self learning predictor; system marginal price; Accuracy; Artificial neural networks; Electricity; Forecasting; Machine learning; Predictive models; Training; Evaluating; Forecasting; Neural network; System marginal price;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Conference_Location

Jinan

Print_ISBN

978-1-4244-6712-9

Type

conf

DOI

10.1109/WCICA.2010.5554330

Filename

5554330