Electricity price forecasting model based on chaos theory

This paper proposes an electricity price forecasting model based on chaos theory. First the chaotic feature of electricity price is verified with the chaos theory. The Lyapunov exponents and the fractal dimensions of the attractors are extracted. Here it can be seen that the electricity price possesses chaotic characteristics, providing the basis for performing the short-term forecast of electricity price with the help of the chaos theory. Then an accurate phase space is reconstructed by multivariable time series constituted by electricity price and its correlated factors, i.e., the system load and the available generating capacity time series. By tracing the evolving trend of the adjacent phase points in the phase space, the global and local electricity price forecasting models based on the recurrent neural network are established, with which the electricity prices in the New England electricity market are successfully predicted