Carbon allowance price is an important tool to reduce carbon emissions and achieve carbon neutrality. It is necessary to establish a predictive model to provide accurate and reliable information to managers and participants in the carbon trading market. Therefore, a novel probability density prediction model, called TS2Vec-based distribution Transformer (TDT), is proposed. TDT consists of two stages: contrastive unsupervised pre-training and supervised training. In the contrastive unsupervised training stage, time series to vector (TS2Vec) is used to represent the dynamic trends and unique features of the data. Then, these representations are fed into the distribution Transformer (DT) to fit the hypothetical probability distribution. Experimental results show that the prediction results of the proposed TDT are more accurate and reliable than other benchmark models. In addition, our research indicates reliable probability density predictions provide enterprises with opportunities to control carbon emission costs and increase economic returns, thereby improving the competitiveness of enterprises and promoting carbon emission reduction.

中文翻译：

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基于 TS2Vec 和配电变压器的碳配额价格概率密度预测


碳配额价格是减少碳排放、实现碳中和的重要工具。有必要建立一个预测模型，为碳交易市场的管理者和参与者提供准确可靠的信息。因此，提出了一种新的概率密度预测模型，称为基于 TS2Vec 的分布变压器 （TDT）。TDT 包括两个阶段：对比无监督预训练和监督训练。在对比无监督训练阶段，时间序列到向量 （TS2Vec） 用于表示数据的动态趋势和独特特征。然后，这些表示被馈送到配电变压器 （DT） 中，以适应假设的概率分布。实验结果表明，所提出的 TDT 的预测结果比其他基准模型更准确、更可靠。此外，我们的研究表明，可靠的概率密度预测为企业提供了控制碳排放成本和增加经济回报的机会，从而提高了企业的竞争力，促进了碳减排。