基于EMD-PSO-LSSVM的碳排分解集成预测方法

二氧化碳排放量的发展趋势作为能够反映各国减排措施的指标之一,近些年来受到广泛关注.为了缓解碳排放数据的非线性和波动性对预测精度造成的影响,提出一种高效的分解集成预测方法用于预测二氧化碳的年排放量.碳排的原始序列数据被经验模态分解(empirical mode decomposition, EMD)方法分解为不同频率的振动模块和残差项,粒子群优化算法(particle swarm optimization, PSO)优化后的最小二乘支持向量机(least squares support vector machine, LSSVM)用于预测每个分解模块.选取世界上12个国家的真实碳排数据进行实例验证,预测结果表明:EMD方法能够有效提高碳排预测的精准度;与其他预测模型相比,分解集成预测方法能够将平均绝对误差(mean absolute error, MAE)的均值最少提高46.46%,最多提高90.09%,将平均Pearson相关系数(Pearson correlation coefficient, PCC)值最少提高10.45%,最多提高45.10%.

A decomposition-integration forecasting method of carbon emission based on EMD-PSO-LSSVM

The emission of carbon dioxide has received much attention in recent years, as it can reflect the effectiveness of those low-carbon measures. To alleviate the nonlinearity and volatility of the annual carbon dioxide emissions, which may affect the forecast accuracy, this paper proposes an efficient decomposition-integration forecasting method to forecast the annual carbon emissions. The empirical mode decomposition (EMD) is used to decompose the original emission series into intrinsic oscillatory modes and a residual with different frequencies. The least squares support vector machine (LSSVM) is optimized using the particle swarm optimization (PSO) algorithm to predict each decomposed part. This paper chooses the real annual carbon emission of 12 countries all over the world to do the case study. The forecasting results indicate the validity of the EMD on improving the accuracy of carbon emission prediction. Furthermore, the comparison results between the EMD-PSO-LSSVM method and other forecasting models show the EMD-PSO-LSSVM can improve the average accuracy of the mean absolute error (MAE) at least 46.46% and at most 90.09%, and can improve the average Pearson correlation coefficient (PCC) at least 10.45% and at most 45.10%.