The Chaotic Identification And Shrot-term Forecasting Of Power Load Time Series

Power industry is a basic industry which is highly related with the nation's development and the people's livelihood, and the forecasting of power time series is significant for power system to guarantee the safe operation and to achieve maximum benefits. For example, accuracy power load forecasting is the basis for dispatch department to formulate power generation scheduling, which guarantees power grid running in a reasonable, safe and stable condition; then the forecasting of average power sale price can help power enterprise to estimate future earnings and to adjust marketing strategy aimed to each power segment market, so as to achieve double harvest of social benefits and economic benefits. Based on research results in recent years, the power time series always shows chaotic characteristics to some extent, so chaotic time series forecasting algorism has been a new choice and research focus of power time series forecasting.Chaotic identification of time series is precondition of chaotic forecasting. The article introduces some accustomed chaotic identification method, including the direct method and the indirect method. The direct method calculates some characteristic parameter, then uses the value as the criterion for judging time series' chaotic feature. Correlation dimension, Kolmogorov entropy, and Lyapunov exponent are main chaotic characteristic parameters, but the application of direct method is restricted for power time series which length is limited and contaminated by noise; the indirect method is surrogate data method, the method is based on some null hypothesis, then does a hypothesis test for the time series. The method identifies non-linear feature by means of the obviating way, but it is not suitable for chaotic identification of cyclical power load. The article improves PPS algorism, proposes a new surrogate data generating method, the method modifies the problems of PPS about surrogate data linearization and information loss, and uses Lempel-Ziv complexity which is more robust instead of correlation dimension as test statistics. By comparing the Lempel-Ziv complexity' evolution law of power load, surrogate data and typical chaotic time series and cyclical time series, the evidence that chaos does exists in power load series has been detected. Combined with calculating of Lyapunov exponent, the accuracy rate power load chaotic identification has been improved.Packard and Takens propose phase space reconstruction theory. The theory proves a low-dimensional phase space can be constructed by calculating embedding dimension and times lag, then the chaotic attractor can be resumed in this space, which lays the foundation for forecasting of chaotic time series. The article introduces the usual calculating methods for embedding dimension and times lag. The forecasting method of chaotic time series can be divided into global method and local method. Because of higher forecasting accuracy and smaller computation, the local method has been widely used. The core of local method is to determine reference neighborhood which are similar as datum phase points, then forecast according to evolution law of reference neighborhood phase points. In traditional local method, Euclid distance is used as a criterion to determine reference neighborhood, but Euclid distance just can reflect space distance between phase points. For high-dimension phase space, it is difficult to describe similarity between phase points by means of Euclid distance. So for high-dimension phase space, the forecasting accuracy of local method decreases rapidly. Aiming at problems above, the article proposes a chaotic local adding-weight linear forecasting algorism based on included angle cosine,the algorism determine reference neighborhood by means of included angle cosine instead of Euclid distance, and regard phase points as vectors. In the process of linear regression parameters identification, uses module and included angle of vectors as optimization objectives instead of Euclid distance, thus the disadvantage of chaotic local forecasting algorism based on Euclid distance is overcame.Average power sales price is a comprehensive statistic, because it covers a wide range and contains rich content, the forecasting of average power sales price is always difficult. There are two main forecasting ideas, one is forecasts time series directly, but because adjustment of power price, the evolution law of average power sales price series will change, so historical time series can not regard as a forecasting basis; the other is forecasting power sales corresponding to various power price types based on formula of average power sales price at first, then synthesizes forecasting value of average price, but the Chinese power price system is very complex, the power sales corresponding to some power price type is quite small which does not show statistic law, so it is difficult to forecast based on this idea, and the computation is considerable. The article proposes a average power sales price forecasting algorism based on power market segmentation, which proves that the average power sales price's forecasting of the whole power market can be transformed into average price contribution rate's forecasting and average power sales price's estimation of each power segment market. For the average price contribution rate's forecasting, uses chaotic adding-weight local linear algorism based on included angle cosine proposed by this article, for average power sales price's estimation, then calculates based on catalogue power price system. Verified by example of average power sales price's forecasting of Hunan province, the forecasting accuracy is fairly high, which can reflects the effect that the power price's change acts on average power sales price, and it's computation is smaller. The algorism has been applied in power marketing analysis and forecasting system of Hunan province, and applied software copyright, which is praised by the users.