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| 摘要: |
| 介绍了在批量处理时间序列情况下，BP神经网络辨识预测电力负荷的方法和步骤。网络成批训练，是权重矢量和偏导数矢量都同时与所有训练矢量的变化成正比地改变。由于采用附加动量项和自适应率等措施，克服了BP规则的局限性，加快了训练速度，增强了网络的泛化能力。在此基础上对某地区实际电力负荷进行了预测，取得了满意的结果。 |
| 关键词: 人工神经网络 电力负荷预测 泛化能力 配电系统 |
| DOI： |
| 分类号:TM715 TP183 |
| 基金项目: |
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| Power load forecast using artificial neural network |
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ZHANG Guo zhong 1 XIONG Wei 1 XIANG Qiu xin 2 HUANG Xiao ming 1 LIU Ya 1
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| Abstract: |
| The method and steps of BP (Back Propagation) neural network for recognizing and forecasting power load in batch data processing of chronological sequence is presented. Batch training of network makes weight vectors and its partial derivative vectors proportionally follow the change of all training vectors. The application of additional momentum and adaptive learning rate overcomes the limitation effect of BP rule, accelerates the training speed and strengthens the generalization ability of network. The real power load of a district is forecasted based on it and the satisfied results are achieved. |
| Key words: artificial neural network,power load forecast,generalization ability |
