基于云模型的电力系统负荷组合预测

摘要

The accuracy of load forecasting of power system is the guarantee of the safe, stable and efficient operation of the power grids. Least squares support vector machine (LSSVM) is widely used on the load forecasting of power system, but this method has many shortcomings in dealing with uncertainty problems. In order to improve the accuracy of selecting the parameters of the kernel function, to deal with uncertainty factors and improve the accuracy of short-term load forecasting, this paper proposes a new model which is combined by the cloud model, particle swarm optimization (PSO) and LSSVM. First of all, through analyzing uncertainty of each influence factor, it uses the models of Cloud-LSSVM and PSO-LSSVM separately to predict the impact factor according to the uncertainty, then it achieves the final forecast through the weighted combination model. At last, the simulation of experiment proves that the new model can achieve better load forecast of power system by dealing with the uncertainty factors.%电力系统负荷预测的精确度决定着电网安全稳定、高效的运行。最小二乘支持向量机(LSSVM)被广泛应用电力系统负荷预测上，然而该方法在处理不确定性问题上有很多不足之处。为了更精确的选择核函数的参数，处理不确定性因素，提高短期负荷预测的精度，提出了一种将云模型、粒子群优化(PSO)和LSSVM相结合的组合模型。首先通过对各影响因子的不确定性分析，按不确定性高低将各影响因子分别应用 Cloud-LSSVM 和PSO-LSSVM 进行预测，然后通过组合模型的加权计算的得到最终预测值。最后，通过仿真对比证明该模型能更好的处理不确定性，从而提高电力系统短期负荷预测精度。