Chaotic Analysis And Prediction For Electric Power Load

The utilization of water resources is relative to electric power load forecasting, which could provide useful data for sustainable utilization of water resources, optimum allocation and reservoir dispatch. Electric power load forecasting also plays a very important role in the safe and economic operation of power system. Electric power load is influenced by many factors. So its behavior appears as the characteristics of complexity, uncertainty and non-linearity. Chaos is looked as the solution with internal stochastic property in the nonlinear deterministic systems. It's very significant work to research on how to obtain and use colorful dynamical information hidden in chaotic time series. Based on the chaos theory, chaotic characteristic of power load time series is analyzed and its forecasting methods are studied in this paper. Some research achievements have been obtained as following.The influence of phase space parameters on phase space quality and the methods for determining delay time & embedding dimension are discussed on the reconstruction theory. Then identification and prediction approaches for chaotic time series are sum up.For electric power load of different time scale including hourly load, daily load and monthly load, quantitative calculation about saturation correlation dimension, maximum Lyapunov exponent and Kolmogorov entropy of power load is used to identify their chaotic characteristics, and conclude that power load time series belong to chaotic series. Moreover, the fractal dimensions of strange attractor in load phase space are estimated, and their phase diagrams are presented.Further work is studying short term load forecasting using neighbor model, linear regression model and Lyapunov exponent model in the phase spaces. At the same time the influence about different embedding dimensions and prediction time on forecasting result is considered. The prediction results indicated that the chaotic phase space model is effective for short term load forecasting.A new chaotic method is successful used for multiple time scale analysis of electric power load. After researching the chaotic characteristic of decomposition coefficient that annual load with long time scale was decomposed to monthly load with short time scale, it shows that decomposition coefficient series is a chaotic one. Phase space model is used for forecasting decomposition coefficient, and prediction result is used for calculating monthly load. The study proves feasibility for applying chaotic analysis on downscaling calculation.Leading artificial neural networks and Kalman filter technique into chaotic phase space, this paper presents two combined models. One is a BP neural networks model based on chaotic analysis. The other is a chaotic Kalman filter model combined the chaotic method with real-time adjustment technique. The principle of building model and forecasting steps about new method are explored in detail. Then application on short term forecasting of daily load based on the combined models is discussed. The prediction result shows that the new combined models could get high precision.Above all, the main study for electric power load focuses on three aspects: about chaotic characteristic analysis, about nonlinear forecasting, and about combined chaotic models. Not only can the research provide practicable method for load forecasting, but also it suggests a new idea for further work. Moreover, it provides a valuable scheme for studying other hydrologic variable.