统计和机器学习模型在近实时的日排放预测中的比较研究
A comparative study of statistical and machine learning models on near-real-time daily emissions prediction
摘要：
二氧化碳排放量的急剧上升是全球变暖和气候变化的主要原因，对人类生存构成巨大威胁，对全球生态系统产生深远影响。因此，及时准确预测和分析其变化趋势，有效控制二氧化碳排放，为二氧化碳减排措施提供参考是十分必要的。本文基于2020年1月1日至2022年9月30日中国所有行业（电力、工业、地面交通、居民、国内航空、国际航空）的单变量日时间序列数据，选择合适的模型来预测近实时的日排放量。提出了6种预测模型，包括3种统计模型：灰色预测（GM（1，1））、自回归移动平均（ARIMA）和带外生因子的季节自回归移动平均（SARIMAX）; 3种机器学习模型：人工神经网络（ANN）、随机森林（RF）和长短期记忆（LSTM）。为了评价模型的性能，引入了均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分误差（MAPE）和决定系数（）五个指标，并对这五个指标进行了详细讨论。结果表明，3种机器学习模型的性能优于3种统计模型，其中LSTM模型在日排放量预测的5个标准值上表现最好，MSE值为3.5179e-04，RMSE值为0.0187，MAE值为0.0140，MAPE值为14.8291%，MAPE值为0.9844。
英文摘要：
The rapid ascent in carbon dioxide emissions is a major cause of global warming and climate change, which pose a huge threat to human survival and impose far-reaching influence on the global ecosystem. Therefore, it is very necessary to effectively control carbon dioxide emissions by accurately predicting and analyzing the change trend timely, so as to provide a reference for carbon dioxide emissions mitigation measures. This paper is aiming to select a suitable model to predict the near-real-time daily emissions based on univariate daily time-series data from January 1st, 2020 to September 30st, 2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic Aviation, International Aviation) in China. We proposed six prediction models, which including three statistical models: Grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX); three machine learning models: artificial neural network (ANN), random forest (RF) and long short term memory (LSTM). To evaluate the performance of these models, five criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination () are imported and discussed in detail. In the results, three machine learning models perform better than that three statistical models, in which LSTM model performs the best on five criteria values for daily emissions prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value, 14.8291% MAPE value and 0.9844 value.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
