关键词: 配电系统, 空间负荷预测, 负荷元胞, 深度学习, 深度置信神经网络（DBN-DNN）, 多源信息融合

Abstract: 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.

Key words: distribution system, spatial load forecasting, cell load, deep learning, deep belief network-deep neural network （DBN-DNN）, multi-source information integration