基于PSR和DBN的超短期母线净负荷预测

随着电网优化调度的精细化、智能化和计及电力系统安全性与经济性的电网高级应用的广泛采用及分布式能源的大量接入,母线负荷预测的精度要求不断提高而负荷的不确定性和非线性特征进一步增强。针对上述问题,文中提出一种基于相空间重构(PSR)和深度信念网络(DBN)的超短期母线负荷预测模型,首先采用C-C法对净负荷时间序列进行PSR,然后利用DBN对重构后的数据进行拟合并得出负荷的预测值。文中利用某市变电站实测负荷数据检验了该超短期母线负荷预测模型的有效性,证明该模型在分布式电源渗透率较高且母线负荷波动较大的情况下仍然有较高的预测精度。

Ultra-short-term bus net load forecasting based on phase space reconstruction and deep belief network

With the refinement and intelligentization of power grid optimization and the extensive adoption of advanced applications of power grid security and economy, and the large-scale access of distributed energy, the accuracy requirements of bus load forecasting are constantly increasing while Uncertainty and nonlinear of the load are further enhanced. Aiming at the above problems, this paper proposes an ultra-short-term bus net load forecasting model based on phase space reconstruction and deep belief network. Firstly, the phase space reconstruction of the original time series is carried out by C-C method, and then the reconstructed data is fitted by the deep belief network to obtain the predicted value of the load. In this paper, the effectiveness of the proposed ultra-short-term bus load forecasting model is tested by using the measured load data of a substation in a city. It is proved that the proposed model still has high prediction accuracy under the condition of high distributed power penetration rate and large fluctuation of bus load.