基于计算机智能的深度信念网络的组合电力负荷预测方法（英文）

Based on deep belief network computer intelligent power load combined forecasting method

电力系统负荷预测是电力系统规划和经济政策制定的主要依据,然而现有的基于计算机人工智能的电力系统负荷预测多采用组合预测方式,其预测精度低,效率低下;针对此问题,提出了一种基于深度信念网络的组合负荷预测方法,此方法首先建立了深度信念网络训练模型,将组合数据与实际负荷数据之间构建的非线性函数关系应用到此训练模型中,通过数据训练,优化深度信念网络层数和参数;使得训练好的组合深度信念网络具有预测能力。利用实际历史数据,对组合负荷预测的精度进行了计算,实验结果表明:所提出的预测方法相对于传统的组合预测方法,具有较高的预测精度。

Power system load forecasting is the main basis of power system planning and economic policyformula- tion. However, the existing power system load forecasting combined forecasting methods, the prediction accuracy and efficiency are low; Aiming at this problem, this paper proposes a combined load forecasting method based on the deep belief network. First deep belief network training model is established, the nonlinear function relation be- tween the combination data and the actual load data is applied to the training model. Through data training, deep belief network layer and the parameters areoptimized,making trained of the combineddeep belief network has the ability to predict. Using actual history data, the accuracy of the combined load forecast is calculated,. And the ex- perimental results show that the proposed combination forecast method compared with traditional forecast methods, has high prediction accuracy, and its computational complexity is low.