Using Chaotic Time Series Prediction Technology For Short-term Electricity Market Electricity Price Forecast

With the deregulation of Power industry all over the world ,establishing electricity markets to optimize distribution of resources is the trend of Power industry.Electricity Price issues are the key problems in the markets and how to price the special commodity-electricity is essential for the smooth market operation.So using the relative historic data in predicting the future electricity Price is a very meaningful work. Both power system load and price are time series.Theoretically speaking,methods applied to forecast load can be used to forecast price,such as Time Series Analysis,Artificial Neuro Network and Wevelet Transform etc.But electricity price is more difficult to forecast than load because of its inherent characteristics such as volatility. Data Mining or Knowledge Discovery in Databases is a new Artificial Intelligence branch developed since 1990s'.Since it has the capacity to distill connotative knowledge and information from abundant data,data mining has been used widely in various fields.one of the important techniques of data mining--similarity search is used in this thesis. Chaotic theory has been proved to be an important and useful theory algorithm.The natural tightly connection between chaos and ftactureis due to the infinite similarities of strange attractor of chaotic dynamic system.Nonlinear time series analysis based on chaotic theory cross through traditional frame of subjective model,draw out Prediction on the inner rules of chaotic time series data. This thesis advances a short-term price forecasting method based on Lyapunov exponent after comprehensively analyzes the relative factors. affer comprehensively analyzes the relative factors.The method can not...