利用多源信息和深度置信神经网络的配电系统空间负荷预测

准确的空间负荷预测是配电系统精益化规划的基础。在此背景下，提出利用多源信息融合和深度置信神经网络的配电系统空间负荷预测方法。首先，在分析空间负荷元胞多源信息特征的基础上，采用基于程度副词语义标定的结构化方法对负荷元胞的非结构化属性进行结构化处理，以充分挖掘利用负荷元胞数据信息。然后，采用受限玻尔兹曼机方法和反向传播（back propagation, BP）算法相结合学习元胞特征，以提升元胞高维特征提取的性能，并采用训练后的深度置信神经网络预测待规划区域的空间饱和负荷密度。最后，以某城市的区域配电系统为例，对所提出的空间负荷预测方法进行验证；仿真结果表明：在空间负荷预测模型中考虑非结构化信息的影响可以提高空间负荷预测精度，且与现有的一些方法相比，所提方法的预测精度更高。

Spatial Electric Load Forecasting for Distribution Systems Using Multi-source Information and Deep Belief Network-Deep Neural Network

Accurate spatial load forecasting is of great significance for promoting fine planning of distribution systems. A spatial electric load forecasting method for distribution systems is proposed by using multi-source information and the deep belief network （DBN） and deep neural network （DNN） （DBN-DNN）. First, the multi-source information feature of cell loads is analyzed, and then a structured method based on the quantification of degree adverb is utilized to transform the unstructured attributes for digging and using the data information of cell loads fully. Then, both the restricted Boltzmann machine （RBM） method and back propagation （BP） algorithm based feedforward neural network are adopted to learn cellular features for enhancing the performance of extracting high-dimensional features of cell loads, and the spatial saturation load density of the planning area is forecasted by the trained DBN-DNN model. Finally, the distribution system in a part of a city is employed for demonstrating the effectiveness of the proposed spatial load forecasting method. Numerical results demonstrated that more accurate spatial load forecasting results can be obtained with the proposed method by considering unstructured attributes of cell loads or comparing with the some existing methods.