| 基于CNN-LSTM组合模型的碳价预测方法 |
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| 引用本文： | 郭宇辰,加鹤萍,余涛,刘敦楠.基于CNN-LSTM组合模型的碳价预测方法[J].科技管理研究,2023(11):200-206. |
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| 作者姓名： | 郭宇辰 加鹤萍 余涛 刘敦楠 |
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| 作者单位： | 1. 新能源电力与低碳发展研究北京市重点实验室;2. 华北电力大学经济与管理学院;3. 国网上海市电力公司 |
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| 基金项目： | 国家社会科学基金重大项目“面向国家能源安全的智慧能源创新模式与政策协同机制研究”（19ZDA081）； |
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| 摘 要： | 对碳价波动的特征进行分析，说明碳价预测的意义；然后，基于卷积神经网络（convolutional neural network,CNN）与长短期记忆网络（long short-term memory, LSTM）提出一种CNN-LSTM组合模型的碳价预测方法，充分考虑碳价的时序特性，通过改善相关模型，从时序数据中提取特征的能力从而提高预测准确性；最后，通过欧洲能源交易所及我国广州碳市场的碳价实例验证，将CNN-LSTM模型的预测结果与其他常用预测模型对比，结果表明CNN-LSTM模型在碳价预测中具有更高的预测准确性。
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| 关 键 词： | 碳价预测 长短时记忆网络 卷积神经网络 组合模型 |
| 收稿时间： | 2022/11/3 0:00:00 |
| 修稿时间： | 2022/11/21 0:00:00 |
Carbon Price Prediction Method by CNN-LSTM Combination Model |
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| Abstract: | With the official launch of the national carbon emission trading market, accurate carbon price forecasting will help market management institutions to achieve effective regulation of carbon prices and efficient performance of emission control enterprises. Then, based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), this paper proposes a carbon price prediction model based on the combined CNN-LSTM method, which can fully consider the timing characteristics of carbon prices and effectively improve the problem that traditional models cannot extract valid features from time series data. Finally, the carbon price examples of the European Energy Exchange and the carbon market in Guangzhou, China are carried out, and the proposed method compared with other common prediction model, and the results show that the carbon price prediction method proposed in this paper has higher prediction accuracy for carbon price prediction at home and abroad. It provides a certain reference for the future research model selection of carbon price prediction. |
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| Keywords: | carbon price prediction long and short time memory convolutional neural network combined model |
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