摘要
在碳交易背景下,对电力企业进行实时、准确、全面的碳排放计量是开展发电结构调整、技术创新、供需联动、碳交易等工作的基础。受制于数据采集与传输系统的限制,动态碳排放因子的测算与预测目前仍难以完全实现。采用深度学习方法,将双重注意力机制与传统的门控循环单元(GRU)神经网络融合,构建了GRU-Attention预测模型。以合肥市2022年的电力数据为样本,结合合肥市平均气象数据,对GRU模型、长短时记忆(LSTM)模型、基于双重注意力机制的LSTM-Attention模型和GRU-Attention模型进行训练,以实现小时级别的碳排放因子预测。利用不同的评价指标对4种预测模型进行对比,与GRU,LSTM,LSTM-Attention模型相比,GRU-Attention模型预测精度更高,有助于实现发电碳排放因子的中长期预测。
With the support of carbon trading policy,the real-time,accurate and comprehensive measurement on power enterprises'carbon emissions is the basis for structure adjustment,technological innovation,supply and demand side interaction and carbon trading of power generation industry.The calculation and prediction on dynamic carbon emission factors is still limited by the data collection and transmission system.By taking deep learning,a prediction model,called GRU-Attention model,was built by combining dual attention mechanism with traditional Gate Recurrent Unit(GRU)neural network.Then,a GRU model,a Long Short-Term Memory(LSTM)model,a LSTMmodel based on dual attention mechanism(LSTM-Attention)and a GRU-Attention model were constructed and trained by the power data of Hefei in 2022 and average meteorological data of Hefei,to achieve hourly prediction on carbon emission factor.Comparing the prediction results made by the four models above,it is found that the prediction made by the GRU-Attention model is more accurate than that of the other three models,which can advance the mid-and long-term prediction on carbon emission factor.
作者
李方一
李楠
周琰
谢武
LI Fangyi;LI Nan;ZHOU Yan;XIE Wu(School of Management,Hefei University of Technology,Hefei 230009,China;Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development,Hefei University of Technology,Hefei 230009,China)
出处
《综合智慧能源》
CAS
2023年第8期11-17,共7页
Integrated Intelligent Energy
基金
国家自然科学基金项目(71902051)。
关键词
碳排放计量
碳排放因子
门控循环单元
双重注意力机制
深度学习
中长期预测
measurement of carbon emissions
carbon emission factor
Gate Recurrent Unit
dual attention mechanism
deep learning
mid-and long-term prediction
作者简介
李方一(1985),男,副教授,博士,从事大数据与能源环境管理等方面的研究,fyli@hfut.edu.cn。
