Title :
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
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713442
