Chaotic Analysis And Prediction For Power Short-term Load Time Series

Short-term load forecasting(STLF)is an important and basal component in the operation of any electric utility whose accuracy directly influence power system's security, profit and quality. Because of the highly nonlinear, complexity and indeterminacy of the power load, means that the traditional load time series pretreatment technology can not be achieved satisfactory results. Chaos is a quasi-stochastic phenomenon appearing in deterministic nonlinear dynamic system. With the development of nonlinear chaotic dynamic, there are more profound recognitions to the complexity of time series. Especially the analysis of time series is becoming an important research aspect that provides a scientific basis for analysis and prediction of short load time series.Based on the chaos theory, characteristic of short-term load time series is analyzed and its forecasting methods are studied in this paper. The main contents discussed in this thesis are described as follows:1. 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. About selection of reconstruction parameters, on the one hand, False Nearest Neighbors method is improved by Cao method, and accurate phase reconstruction parameters of short-term load; on the other hand, improved C-C method is introduced into selection of reconstruction parameters. The results of the two methods were mutually supplemented and verified.2. For electric hourly load, on the one hand, short-term load is analyzed qualitatively be Cao methed; on the other hand, quantitative calculation about saturation correlation dimension, the largest Lyapunov exponent and Kolmogorov entropy of power load is used to identity their chaotic characteristics. In addition, it is found that the calculation of correlation dimension is seriously influenced by many factors. Through introducing non subjective approach, to avoid the limitations of judging whether time series has chaotic character or not by the invariants are pointed out.3. Large computational quantity and cumulative error are main shortcomings of add- weighted one-rank local-region method for prediction of chaotic time series. Local-region multi-steps forecasting model based on phase reconstruction is adopted for short-term load time series prediction. When using the method for the STLF of a certain power network in the north china, 1998. The chaotic characteristics of the load time series in this case are analyzed, and the phase space reconstruction parameters are deduced. Local-region multi-steps forecasting model and the largest Lyapunov method are used in the forecasting experiment for the load in one day and one week. The prediction results indicated that the chaotic model is effective for short term load forecasting.4. Studied on the predictable size of chaotic time series, a method of direct multi-step prediction of Short-term load is proposed, which is based on the average predictable size and radial basis functions neural networks. And short-term load that have been used in this paper is predicted by the method. Simulation results for direct multi-step prediction method could get high precision.