配电网大数据环境下的多点负荷预测算法与应用研究

Research and application of multi-node load forecasting algorithm under the environment of distribution network's big data

随着配网规模日益庞大、负荷类型日趋多样化、数据数量与类型的快速增加,将具有小规模特征明显的负荷与大规模整合性质的负荷进行联合预测工作,形成有效的上下级预测网络,对配网规划运行具有重要作用。考虑配网测量设备数量对负荷预测的数据量限制,提出一种"从整体到节点"的多点负荷预测方式。针对AR无法接纳多源数据与BP神经网络算法结果受相似日影响大等弊端,提出一种优势互补的AR-ANN算法。最后,分别通过普通单节点负荷预测、传统"从节点到整体"多点负荷预测与新多点负荷预测的算例研究,结果验证了AR-ANN在数据处理速度、预测误差等方面的优势。

With the increasing scale of distribution network, more diversity of load types, larger volume and more types of data, forming an effective up-and-down forecast network, which means to combine small-scale and characteristically obvious load forecast with large-scale and integrated load forecast, is important to distribution network planning and operation. Considering the limitation of the data quantity for the lack of distribution network measurement equipment, this paper proposes a multi-node load forecasting method of＂from the whole to the nodes＂. Because AR is not able to accept multiple source data and the outcome of BP neural network algorithm is severely affected by the similar day, this paper proposes a novel algorithm, AR-ANN, with complementary advantages of the previous two. Finally, the strength of AR-ANN in data processing speed and prediction error is validated respectively by tests of an ordinary single node load forecast, a traditional multi-node load forecast by the method of ＂from the nodes to the whole＂ and the new way of multi-node load forecast.