粒子群优化BP算法在短期负荷预测中的应用

Application of the PSO-BP Algorithm in Electric Power System Short-term Load Forecast

电力系统短期负荷预测是电力系统调度运营和用电服务部门的重要日常工作之一,其预测精度直接影响到电力系统运行的安全性、经济性和供电质量。为提高预测精度,本文引入一种新型的群智能方法--粒子群优化算法,并将这种智能算法与BP算法相结合,形成了粒子群优化BP算法模型,建立了计及气象因素的短期负荷预测模型。通过具体算例将此模型与单纯的BP模型进行比较,结果表明:该算法具有较高的预测精度,完全能满足实际工程的要求。

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. For improving the accuracy of the load forecasting, in this paper, a PSO-BP hybrid algorithm is formed, which is the combination of PSO and BP algorithm. 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.