Power System Short-term Load Forecasting Based On Support Vector Machine (svm)

With the development of power market, the mechanism of competition has replaced the monopoly in the conventional electricity market gradually. At the same time, the manner of time-varying prices gets a promotion. Short-term load forecasting provides important foundation for the safety and economical operation of power system. According to the situation of the load changing, Power companies can make the plan of power generation and supply planning timely. The operation of power marker requires high precision of short-term load forecasting for the minimal cost of the power system operation. The operation of power marker can arrange the plan of power, power supply and power distribution timely and accurately. New theory and new technology based on load forecasting researches have been developed continuously. As a new technology of data mining, support vector machine various factors of non-linear characteristics affecting power, there is a research about the method of short-term load forecasting based on support vector machine by using its advantages of non-linear processing and generating ability.The application profiles of the support vector machine in the field of short-term load forecasting are comprehensively summarized in this thesis. The thesis introduces the knowledge of short-term load forecast clearly. In order to make a complete theoretical preparation for the future work, which analyzes various of prediction methods, advantages and disadvantages. It introduces the polynomial regression model and SVM model respectively, and then designs the specific prediction program for the two models. Starting from the principle of support vector machine and compared with other method, the superiorities of the support vector machine method in the application of short-term load forecasting a series of support e elaborated. At the same time, some problems about the application of support vector machine, including data pre-processing, the constructing and selection of kernel function, and parameter optimization method, are analyzed in the thesis and the current solutions are provided respectively. In particular, for a series of support vector machine-based improvements and some mixed forecasting methods consisting of support vector machine with other algorithms, a comprehensive summary is given, from the perspective of the mechanism about support vector machine algorithm being applied into load forecasting, and the elevation of prediction accuracy and speed. Meantime, some key issues needing further discussion are put forward. Finally, this thesis summarizes the key issues about short-term load forecasting based on support vector machine, and gives some recommendations. The simulation results show that the comparison that SVM model has its obvious advantages in the short term forecast. At the same time, this paper achieves predictive simulation interface and calculation by the combination of VB and MATLAB advantages.