基于WT-IPSO-BPNN的电力系统短期负荷预测

Short-term load forecasting based on WT-IPSO-BPNN

鉴于短期负荷预测精度对电力系统安全、经济和可靠运行的重要性,为提高预测精度,本文提出了基于小波分解(WT)、改进粒子群算法(IPSO)和BP神经网络的组合预测模型。首先运用小波分解对负荷数据预处理,将历史数据分解成cd1、cd2、cd3以及ca3;然后对分解后的小波序列分别进行神经网络建模和预测;最后小波重构负荷序列的最终预报。为提高BP神经网络所需样本的精确性和神经网络的收敛速度及稳定性,采用改进粒子群算法优化网络,形成了“分解-预测-重构”模型。经实例验证,与小波分解BP神经网络方法相比,本文所提方法具有训练学习能力更强、收敛速度更快、预测精度高和适应能力更强的优点。

In order to improve the accuracy of short-term load forecasting,a combined forecasting model based on wavelet decomposition(WT),improved particle swarm optimization(IPSO)and BP neural network is proposed.Firstly,we use wavelet decomposition to preprocess the load data,and decompose the historical data into cd1,cd2,cd3 and ca3;then we use neural network to model and predict the decomposed wavelet sequence;finally,we use wavelet to reconstruct the final forecast of the load sequence.In view of the accuracy of BP neural network samples and to increase the convergence speed and stability of the neural network,the improved particle swarm optimization method is used to optimize the network,forming a“decomposition prediction reconstruction”model.Compared with the wavelet decomposition BP neural network method,it has stronger training and learning ability,faster convergence speed,high prediction accuracy and strong adaptability.