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Research On Power Short-Term Load Forecasting Model And Method Based On Data Mining

Posted on:2005-08-23Degree:DoctorType:Dissertation
Country:ChinaCandidate:Q Y ChengFull Text:PDF
GTID:1102360152465623Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
Power short-term load forecasting(STLF) is an important and integral component in the operation of any electric utility whose accuracy directly influence power system's security, profit and quality. STLF is characterized by massive data for forecasting, noisy-contained sample data, influenced by weather condition, and randomicity. Based on various data mining technologies, the author aims at each stage of STLF and has done deep research on the pre-process of historical load data, characteristic of load sequence, process of weather condition, establishment of forecasting model and its input parameters mining. All these work has laid a solid foundation for hi-accuracy STLF software development.In order to purge the historical load data, this paper brings forward an intelligent and dynamic purging model for dirty data based on clustering and sorting thinking in data mining, and improves the Kohonen neural network by using fuzzy soft clustering thinking which enables parallel calculation of fuzzy C-means clustering. A new dynamic algorithm for calculation is put forward which can automatically fix new clustering center according to the update of sample sets. The improved Kohonen neural network, along with RBF neural network make up this fast and dynamic purging model which makes sure the accuracy for future prediction.The principal-element analysis method is used to process the historical load data which effectively makes information more centralized due to so much daily -load data. It is also improved so that the information can remain in the process of data standardization. Forecasting model based on this foundation, which only needs few key components for the complicated neural network modeling and simple calculation for the rest components, has apparent main part and can greatly improve both modeling efficiency and forecasting accuracy.In order to seek properties of load data, Lyapunov exponents of load sequence and theoretic value which can predict time are computed which provide theoretic support for forecasting task. The Lyapunov exponents indicate load time sequence has chaos characteristic. Meanwhile, several weather factors (Effective Temperature, Temperature Humidity Index ,Chillness Humidity Index ,Comfort Index ), which reflect the effort of temperature, humidity and wind power on human, are introduced to evaluate the change of STLF under weather condition. By comparing with sole temperature factor, itdemonstrates the rationality of weather factors' introduction.Rough Set reduction algorithm in data mining is used to solve input parameter choosing which can enable the confirmation of key property combination and structure construction. Input parameter choosing is a big problem in neural network modeling which imposes great influence on the accuracy of forecasting. In order to reduce the complication of rough set reduction algorithm based on normal rough set division function, RAPHF- a reduction algorithm based on property-first illuminating function is put forward. On the base of RAPHF, a new algorithm with incremental processing function named RAPHF-I is proposed which ensures the rationality and accuracy of input parameters because power STLF is considered as a dynamic course with sample data updated ceaselessly.Workaday load(weekend included also) forecasting is finished by forward neural network. BP-AA, a learning algorithm based on parameter-changeable activation function, is advanced to solve normal BP's low convergence speed, and BP-AAEC-a double searching method of Logistic chaos mapping is introduced to overcome the BP's partial minimum fault. According to the test, algorithm construct in this paper can greatly improve convergence speed, effectively solve the partial minimum problem, and avoid the long searching time of Logistic chaos. Power load forecasting in important festivals like New Year, Spring Festival, May Day and the National Day which have a long forecasting period, lack of reference historical data and are prone to be influenced by weather condition is carried out by using GM(1,1) combi...
Keywords/Search Tags:STLF, data mining, compositive weather index, rough set, neural network, gray model
PDF Full Text Request
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