基于弹性网降维及花授粉算法优化BP神经网络的短期电力负荷预测

电力负荷预测为电力系统规划和运行提供可靠的决策依据。随着智能电网的全面发展,数据采集与监视控制系统(SCADA)获取数据量增加,数据的结构也更加复杂,负荷的频繁变化以及地区性的气象因素等都将影响负荷的预测的准确性。提出一种弹性网(EN)进行大数据降维以及花授粉算法(FPA)优化BP神经网络的短期电力负荷预测方法。首先采用弹性网对负荷和气象等高维大数据进行选择和降维。弹性网通过在惩罚项中添加L1范数和L2范数,兼具了最小绝对值收缩及变量选择(LASSO)和岭回归的优点,克服了LASSO降维时因为数据内部存在共线性和群组效应而影响降维效果的问题;然后,考虑到BP神经网络权值和阈值容易受到初值的影响、收敛速度慢以及容易陷入局部最优,引入花授粉算法(FPA)优化BP神经网络,通过与粒子群算法(PSO)对比得出花授粉算法寻优速度更快,效果更好。本文方法应用于实际电力负荷预测,结果表明能有效提高预测精度。

Short term power load forecast based on dimension reduction by elastic network and flower pollination algorithm optimized BP neural network

Power load prediction can provide reliable decision making basis for power system planning and operation. With the development of smart grid, the amount of data collected by supervisory control and data acquisition increases largely, and the structure of data becomes more complex. Frequent changes of load and regional meteorological factors have influence on the accuracy of load forecasting. A short term load forecasting method is proposed in this study, which is based on elastic network (EN) for large data dimension reduction and flower pollination algorithm (FPA) for BP neural network optimization. By adding norms and norms to penalty items, the elastic network has the advantages of least absolute shrinkage and selection operator (LASSO) and ridge regression. It can solve the problem of dimension reduction effect, which is affected by collinearity and group effect in LASSO dimension reduction. Then, FPA is introduced to optimize BP neural network, in which the weights and thresholds are easily affected by initial values, slow convergence speed and easy to fall into local optimum. Compared with particle swarm optimization method, the optimization speed of flower pollination algorithm is faster and the effect is better. The proposed method has been applied for predicting power load. Experimental results show that the prediction accuracy can be effectively improved.