云南师范大学学报（自然科学版）2026，Vol.46Issue(2)：20-26,7.DOI:10.7699/j.ynnu.ns-2026-05
基于深度强化学习的绿电交易价格预测研究
Green Electricity Price Forecasting Based on Deep Reinforcement Learning
摘要
Abstract
Aiming to enhance the accuracy of green electricity trading predictions,a green electricity price prediction model based on deep reinforcement learning was proposed by integrating graph struc-ture modeling,temporal feature extraction,and a strategy optimization mechanism.First,a graph conv-olutional network(GCN)was employed to model the structural dependencies among trading entities.Second,gated recurrent units(GRUs)were introduced to extract the dynamic evolution patterns of historical prices and external features.Finally,an improved deep Q-network(DQN)was applied to generate an optimal price prediction strategy under state-aware conditions.Experimental results dem-onstrate that the model outperforms comparative models such as LSTM,GCN-LSTM,and standard DQN across multiple evaluation metrics,achieving an average reduction in MAE of 7.5％and a de-crease in ROI prediction error of 13.2％.Furthermore,the model exhibits strong robustness in scenar-ios involving noisy disturbances and sudden policy changes.关键词
绿电交易/价格预测/深度强化学习/图神经网络/市场策略优化Key words
Green electricity trading/Price forecasting/Deep reinforcement learning/Graph neural net-work/Market policy optimization分类
信息技术与安全科学引用本文复制引用
张阳,敬如雪,康耀堃,李秀秀,王娜..基于深度强化学习的绿电交易价格预测研究[J].云南师范大学学报（自然科学版）,2026,46(2):20-26,7.基金项目
国网甘肃省电力公司科技资助项目(B32708250011). （B32708250011）
