Application Of The Mixed PSO-BP Algorithm And The Rough Set Theory In Electric Power System Short-term Load Forecast

The short-term load forecasting (STLF) of electric power system is one of the important routines for power dispatch and utility departments. It is widely used in the dispatching and operation planning of power systems, and the accuracy of the load forecasting is helpful to the security, economy of power systems and quality of the power supply. The features of short-term load forecasting can be generalized as followings: many data need to be forecasted, the physical factors which influence forecast are complicated and random, and high precision of forecasting is demanded. With the establishment and development of the power market, STLF has been one of the important symbols for the power system modern management. The primary problem of the load forecasting is the selecting of the forecasting technique, namely, how to build a forecasting model which is applicable to the studied district. The study of STLF has been deepened and various methods have been putted forward, along with the development of the technology. The accuracy has been improved continuously, because of the advanced method from the classical Statistic analysis methods to the modern intelligent methods. However, due to the complexity of the forecasting problem, they have some shortages inevitably. Particle Swarm Optimization (PSO) is an evolutionary computation technique based on the swarm intelligence, which is originated from artificial life and evolutionary computation. In this paper, a mixed PSO-BP algorithm is formed, which is the combination of PSO and artificial neural network (ANN). Then, a short-term load forecasting model involving various influencing factors is built. The short-term load forecasting of power system is performed using the mixed PSO-BP algorithm and improved BP algorithm. The simulation results indicate that this mixed PSO-BP algorithm is better than improved BP algorithm. Meanwhile, because the short-term load is influenced by many uncertain factors and the Rough Set theory is good at dealing with uncertain problems. So a novel model based on the rough sets theory is proposed in the paper. The results show that it can satisfy the demand of the practical engineering. Finally, a combined forecasting model is proposed based on the preceding two algorithms. The combined forecasting model can avoid the unilateralism of the single model and reduce the risk of the forecasting.