基于连云港市的电力系统短期负荷预测研究

Research on Lianyungang-based Short-term Load Forecasting of Power System

对连云港市负荷特性深入分析后进行了短期负荷预测的研究,简要介绍了误差反向传播算法即BP算法的结构和原理,并将BP算法用于短期负荷预测,简单高效可行,但由于该算法收敛的时间较长、且容易陷入局部极小点,故在后文提出了用粒子群算法优化BP神经网络的新型算法,该算法能够有针对性地优化网络结构中的权值和阈值,在不断迭代的情况下,使得预测误差向减小的方向训练。实例表明,优化后的模型预测结果准确率有了较大程度提高,满足了负荷预测的基本要求。

Research is made on short-term load forecasting based on analysis on characteristics of load in Lianyungang,the structure and principle of error back propagation algorithm（ BP algorithm） is introduced. The BP algorithm is used in load forecasting,which is simple,efficient and feasible,but since it takes a long time due to the convergence of the algorithm,and it often falls into local minimum points easily,so new optimization algorithm of particle swarm algorithm to optimize the BP neural network is used. The algorithm can be targeted to optimize the weight value and threshold value of the network structure,in case of constant iteration,it works in the direction of reducing the forecast error. The example shows that the prediction accuracy is improved greatly so that it can satisfy the basic requirements for load forecasting.