论文标题
基于上海飞行员市场，用于购买碳排放权策略的混合深度学习方法
A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market
论文作者
论文摘要
碳排放权的价格在碳交易市场中起着至关重要的作用。因此，准确的价格预测至关重要。以上海试点市场为例，本文试图为企业设计碳排放采购策略，并建立碳排放价格预测模型，以帮助他们降低购买成本。为了使预测更加精确，我们通过将广义自动回归有条件的异性恋（Garch）嵌入到栅极复发单元（GRU）模型中，并将其与其他模型的性能进行比较，从而构建了混合深度学习模型。然后，根据冰山秩序理论和预测价格，我们提出了碳排放权的采购策略。结果，具有5天滑动时间窗口的Garch-Gru模型的预测误差是所有六个模型的最小值。在模拟中，基于Garch-Gru模型的采购策略的成本也最低。由混合深度学习方法构建的碳排放采购策略可以准确发送正时信号，并帮助企业降低碳排放许可的购买成本。
The price of carbon emission rights play a crucial role in carbon trading markets. Therefore, accurate prediction of the price is critical. Taking the Shanghai pilot market as an example, this paper attempted to design a carbon emission purchasing strategy for enterprises, and establish a carbon emission price prediction model to help them reduce the purchasing cost. To make predictions more precise, we built a hybrid deep learning model by embedding Generalized Autoregressive Conditional Heteroskedastic (GARCH) into the Gate Recurrent Unit (GRU) model, and compared the performance with those of other models. Then, based on the Iceberg Order Theory and the predicted price, we proposed the purchasing strategy of carbon emission rights. As a result, the prediction errors of the GARCH-GRU model with a 5-day sliding time window were the minimum values of all six models. And in the simulation, the purchasing strategy based on the GARCH-GRU model was executed with the least cost as well. The carbon emission purchasing strategy constructed by the hybrid deep learning method can accurately send out timing signals, and help enterprises reduce the purchasing cost of carbon emission permits.
