He Jiao, Li Yuhang, Xu Xiaochuan, Wu Di
School of International Business and Management, Sichuan International Studies University, Chongqing, China.
College of Computer and Information Science, Southwest University, Chongqing, China.
PLoS One. 2025 Jan 6;20(1):e0313856. doi: 10.1371/journal.pone.0313856. eCollection 2025.
The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain factors. To fill this gap, we propose a hybrid deep learning approach for consumption forecasting of oil and coal in China. It consists of three parts, i.e., feature engineering, model building, and model integration. First, feature engineering is to distinguish the different correlations between targeted indicators and various features. Second, model building is to build five typical deep learning models with different characteristics to forecast targeted indicators. Third, model integration is to ensemble the built five models with a tailored, self-adaptive weighting strategy. As such, our approach enjoys all the merits of the five deep learning models (they have different learning structures and temporal constraints to diversify them for ensembling), making it able to comprehensively capture all the characteristics of different indicators to achieve accurate forecasting. To evaluate the proposed approach, we collected the real 880 pieces of data with 39 factors regarding the energy consumption of China ranging from 1999 to 2021. By conducting extensive experiments on the collected datasets, we have identified the optimal features for four targeted indicators (i.e., import of oil, production of oil, import of coal, and production of coal), respectively. Besides, we have demonstrated that our approach is significantly more accurate than the state-of-the-art forecasting competitors.
石油和煤炭消费预测有助于政府优化和调整能源战略，以确保中国的能源安全。然而，这种预测极具挑战性，因为它受到许多复杂和不确定因素的影响。为了填补这一空白，我们提出了一种用于中国石油和煤炭消费预测的混合深度学习方法。它由三个部分组成，即特征工程、模型构建和模型集成。首先，特征工程是区分目标指标与各种特征之间的不同相关性。其次，模型构建是构建五个具有不同特征的典型深度学习模型来预测目标指标。第三，模型集成是采用定制的自适应加权策略将构建好的五个模型进行集成。这样，我们的方法兼具五个深度学习模型的所有优点（它们具有不同的学习结构和时间约束，以使其多样化以便集成），能够全面捕捉不同指标的所有特征，从而实现准确预测。为了评估所提出的方法，我们收集了1999年至2021年期间中国能源消耗的880条真实数据，涉及39个因素。通过对收集到的数据集进行广泛实验，我们分别确定了四个目标指标（即石油进口、石油产量、煤炭进口和煤炭产量）的最优特征。此外，我们已经证明，我们的方法比最先进的预测竞争对手显著更准确。
