System Marginal Electricity Price Forecasting Based On Wavelet Networks

As the reformation of electric power market in our country, the company of electric power will face the problems of entering the market. It needs a system of optimizing attempters and bidding decision. System marginal electricity price reflects the supply and demand relation of electric power market. The markets in most countries use it in liquidation. The accuracy of the electricity price forecasting is very important to power plants bidding decision.Study several kind common used methods and compare them with each other, we plan to use wavelet neural networks for system marginal electricity price. Wavelet network is a multi-resolution, hierarchical artificial neural network, which is established on the basis of wavelet theory. It not only combines the excellent time-frequency characters of wavelet theory with the self-organizing and self-learning abilities of artificial neural network, but also avoids inherent problems in traditional artificial neural network, such as blind architecture design and local optimization. But we usually use BP arithmetic to train the wavelet networks. Its training speed is slow and can relapse into local optimization, though it is a simple arithmetic and be used abroad. In order to avoid the limitation of the BP arithmetic, enhance predict precision and expedite convergence speed, this paper establishes the electricity price forecasting model using wavelet neural networks based on the genetic algorithm. The model combines the global optimization searching performance of the genetic algorithm and the time-frequency localization of the wavelet neural networks. The examples of function approach and price forecasting using two different methods show that this model can effectively improve the forecasting precision and avoid the limitation of the BP neural networks model.