基于BP神经网络的碳达峰目标下碳汇计量方法研究

Research on Carbon Sink Measurement Method under Carbon Peak Target based on BP Neural Network

  • 摘要: 为了解决当前碳汇计量方法存在计量误差、准确率低的问题,提出了基于BP神经网络的碳达峰目标下碳汇计量方法的研究。首先,基于碳达峰目标对碳汇数据进行预处理,获取准确度较高的碳汇数据;根据BP神经网络构建碳汇计量BP神经网络模型,输出碳汇数据集;然后综合考虑碳库选择指标,选择与碳汇数据集匹配的碳库;最后,实现碳达峰目标下碳汇计量的目标。实验证明,此种计量方法具有较高的拟合度,计算值与原值更加接近,相对误差在1.01%~1.71%,始终保持在2%以下,证明该方法的碳汇计量结果准确率较高。

     

    Abstract: In order to solve the problems of measurement error and low accuracy of current carbon sink measurement methods, a carbon sink measurement method based on BP neural network is proposed. Firstly,the carbon sink data was preprocessed based on the carbon peak target to obtain the high accurate carbon sink data. The BP neural network model of carbon sequestration measurement was constructed to output the carbon sequestration data set according to BP neural network. Then, considering the carbon pool selection index, the carbon pool matching the carbon sink data set was selected. Finally, the goal of carbon sink measurement under the goal of carbon peak was achieved. Experiments showed that this measurement method had high fitting degree.The calculated value was closer to the original value. The relative error was between 1.01% and 1.71%, which was always below 2%. It proves that the accuracy of carbon sink measurement results of this method was high.

     

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