基于IPSO-WNN的综合能源系统短期负荷预测

针对传统小波神经网络(WNN)综合能源系统负荷预测模型存在收敛速度、易陷入局部最优等缺点而导致预测精度不高的问题,文中提出了一种基于改进粒子群(IPSO)的WNN综合能源系统短期负荷预测方法。利用Pearson系数对各影响因素进行分析,选择合适的因素作为综合能源负荷预测的输入量;对传统粒子群算法进行改进,在PSO中引入混沌算法并根据粒子适应度采用不同的粒子惯性权重选择策略;基于IPSO建立了WNN综合能源系统短期负荷预测模型。通过案例验证,基于IPSO的WNN预测模型相比于传统WNN预测模型,预测精度明显提升。

A short term load prediction of integrated energy system based on IPSO-WNN

An IPSO-WNN method for short term load prediction of integrated energy system is proposed to solve the problem that the conventional WNN model has the disadvantages of slow convergence speed and easy to fall into local optimum, which leads to the low prediction accuracy. Firstly, the Pearson coefficient is used to analyze the influencing factors and select the appropriate factors as the input quantity of the prediction. Secondly, the traditional particle swarm optimization algorithm is improved. Chaos algorithm is introduced in PSO and different particle inertia weight selection strategies are adopted according to particle fitness, and then IPSO-WNN prediction model was established based on the improved particle swarm algorithm (IPSO) to realize load prediction of comprehensive energy system. Finally, compared with the traditional WNN prediction model, the ipso-based WNN prediction model has improved the prediction time and accuracy.