随着智能电网的建设,影响短期负荷预测精度的因素也越来越多。针对海量数据本文提出一种基于改进Fisher分数(F-score)特征选择的改进粒子群优化的BP(modified particle swarm optimization and back propa- gation, MPSO-BP)神经网络短期负荷预测方法。首先采用改进F-score特征评价准则计算影响负荷预测精度的各个特征的F-score值,衡量每个特征的重要程度,再通过F-score Area法设定阈值筛选出最优特征子集,然后将最优特征子集作为MPSO -BP神经网络模型的输入变量完成对预测日一天24点负荷的预测,并与MPSO -BP神经网络短期负荷预测和传统的BP神经网络短期负荷预测进行对比。算例表明,本文提出的短期负荷预测方法可以较好地对海量数据进行挖掘,具有较高的预测精度。
英文摘要:
With the construction of intelligent power grid, the factors affecting the accuracy of short-term load forecasting are increasing. In this paper, a short-term load forecasting method of MPSO-BP neural network based on improved F-score feature selectionSis proposed. Firstly, the improved F-score feature evaluation criterion is used to calculate the F-score value of each feature affecting the load forecasting accuracy, and the importance of each feature is measured, Then, the optimal feature subset was selected by the F – score Area method,By using the optimal feature subset as input variable of the MPSO-BP neural network model to complete the forecast of 24 point load, comparing with the short-term load forecasting of MPSO-BP neural network and he traditional BP neural network short-term load forecasting. The calculation example shows that the short-term load forecasting method proposed in this paper can be used to excavate the massive data, which has higher prediction accuracy.