提示
基于bp神经网络模型的山东省碳排放量预测
首发时间：2023-04-18
摘要：在低碳经济发展的背景下，山东省碳排放数据更新迟缓，以往的预测模型难以满足现实需求。因此，我们基于山东省1997年至2020年的历史数据，选取了7项重要的碳排放影响指标，并通过pearson相关系数分析，建立了gm（1,1）灰色预测和bp神经网络模型，以对山东省的碳排放量进行仿真预测。研究结果表明，bp神经网络模型明显优于传统的统计方法，具有更小的误差和更高的精度，更适用于碳排放量及相关指标的预测。预测结果显示，山东省未来的碳排放量呈缓慢增长趋势，为政府决策提供了科学依据。
关键词： 灰色预测
for information in english, please click here
carbon emission prediction of shandong province based on bp neural network model
abstract：in the context of low-carbon economic development, the carbon emission data of shandong province is updated slowly, and the previous prediction model is difficult to meet the realistic demand. therefore, based on the historical data of shandong province from 1997 to 2020, we selected seven important carbon emission impact indicators, and established gm (1,1) grey prediction and bp neural network model through pearson correlation coefficient analysis, so as to simulate and forecast the carbon emission of shandong province. the results show that the bp neural network model is obviously superior to the traditional statistical methods, with smaller errors and higher accuracy, and is more suitable for the prediction of carbon emissions and related indicators. the forecast results show that the carbon emission of shandong province will increase slowly in the future, which provides a scientific basis for the government\'s decision.
keywords： grey prediction
基金：
论文图表：
引用
导出参考文献
no.****
动态公开评议
共计0人参与
勘误表
基于bp神经网络模型的山东省碳排放量预测
评论
全部评论