Leshan Normal University, Leshan, China.
College of Software, Xinjiang University, Urumqi, China.
PLoS One. 2024 Sep 6;19(9):e0307915. doi: 10.1371/journal.pone.0307915. eCollection 2024.
Greenhouse gas emissions, as one of the primary contributors to global warming, present an urgent environmental challenge that requires attention. Accurate prediction of carbon dioxide (CO2) emissions from the industrial sector is crucial for the development of low-carbon industries. However, existing time series models often suffer from severe overfitting when data volume is insufficient. In this paper, we propose a carbon emission prediction method based on meta-learning and differential long- and short-term memory (MDL) to address this issue. Specifically, MDL leverages Long Short-Term Memory (LSTM) to capture long-term dependencies in time series data and employs a meta-learning framework to transfer knowledge from multiple source task datasets for initializing the carbon emission prediction model for the target task. Additionally, the combination of differential LSTM and the meta-learning framework reduces the dependency of the differential long- and short-term memory network on data volume. The smoothed difference method, included in this approach, mitigates the randomness of carbon emission sequences, consequently benefiting the fit of the LSTM model to the data. To evaluate the effectiveness of our proposed method, we validate it using carbon emission datasets from 30 provinces in China and the industrial sector in Xinjiang. The results show that the average absolute error (MAE), Coefficient of Determination (R2) and root mean square error (RMSE) of the method have been reduced by 61.8% and 63.8% on average compared with the current mainstream algorithms. The method provides an efficient and accurate solution to the task of industrial carbon emission prediction, and helps environmental policy makers to formulate environmental policies and energy consumption plans.
温室气体排放是导致全球变暖的主要因素之一，是当前亟待解决的环境挑战。准确预测工业部门的二氧化碳（CO2）排放对于发展低碳产业至关重要。然而，当数据量不足时，现有的时间序列模型往往存在严重的过拟合问题。在本文中，我们提出了一种基于元学习和差分长短期记忆（MDL）的碳排放量预测方法来解决这个问题。具体来说，MDL 利用长短期记忆（LSTM）来捕捉时间序列数据中的长期依赖关系，并采用元学习框架从多个源任务数据集转移知识，用于初始化目标任务的碳排放量预测模型。此外，差分 LSTM 和元学习框架的组合减少了差分长短期记忆网络对数据量的依赖。该方法中包含的平滑差分方法减轻了碳排放量序列的随机性，从而有利于 LSTM 模型对数据的拟合。为了评估我们提出的方法的有效性，我们使用来自中国 30 个省份和新疆工业部门的碳排放量数据集进行了验证。结果表明，与当前主流算法相比，该方法的平均绝对误差（MAE）、决定系数（R2）和均方根误差（RMSE）平均降低了 61.8%和 63.8%。该方法为工业碳排放预测任务提供了一种高效准确的解决方案，有助于环境政策制定者制定环境政策和能源消耗计划。
