The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ...The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.展开更多
针对微电网集群在复杂约束下发电成本偏高和经济效益不足的问题,提出一种基于双种群金豺优化(dual population golden jackal optimization,DGJO)算法的微电网集群优化调度模型。首先,以综合成本最小化为目标,构建涵盖运行、储能、电力...针对微电网集群在复杂约束下发电成本偏高和经济效益不足的问题,提出一种基于双种群金豺优化(dual population golden jackal optimization,DGJO)算法的微电网集群优化调度模型。首先,以综合成本最小化为目标,构建涵盖运行、储能、电力交易及环境等多项成本的微电网集群优化调度模型。其次,提出DGJO算法,利用莱维飞行实现自适应收敛,以双种群策略平衡探索与开发,引入哈里斯鹰围攻和缓存猎取算子提升寻优精度。然后,采用DGJO对时间卷积网络(temporal convolutional network,TCN)和双向门控循环单元(bidirectional gated recurrent unit,BiGRU)的超参数进行优化,提升收敛速度和模型的泛化能力。最后算例结果表明,所提模型在复杂约束与扰动情景下有较好的鲁棒性,并有效降低了系统的综合成本。展开更多
基金funded by the National Natural Science Foundation of China(NSFC)(No.62066024)funded by Basic Scientific Research Projects of Higher Education Institutions in Liaoning Province(LJ212411632063)the National Undergraduate Training Program for Innovation and Entrepreneurship(S202511632045).
文摘The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency.
文摘为提高高超声速滑翔飞行器(HGV)轨迹预测的精度,提出一种基于时域卷积网络(temporal convolutional network,TCN)和双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM)结合的HGV轨迹预测方法.该方法利用TCN的因果膨胀卷积提取HGV轨迹多尺度动态特征,融合BiLSTM的双向循环机制挖掘轨迹长时依赖与上下文关联,通过全连接层将预测结果映射到样本空间.引入贝叶斯优化(Bayesian optimization,BO)与灰狼优化(grey wolf optimization,GWO)组合优化模式,实现了网络超参数的全局优化,据此建立了深度学习框架下的HGV轨迹预测模型.数值仿真结果表明,在训练完备条件下,建立的预测模型能够有效预测HGV未来时刻的位置状态,相较于4种对比模型,该预测模型的均方根误差平均降低62.10%,平均绝对误差平均降低61.66%.
文摘针对微电网集群在复杂约束下发电成本偏高和经济效益不足的问题,提出一种基于双种群金豺优化(dual population golden jackal optimization,DGJO)算法的微电网集群优化调度模型。首先,以综合成本最小化为目标,构建涵盖运行、储能、电力交易及环境等多项成本的微电网集群优化调度模型。其次,提出DGJO算法,利用莱维飞行实现自适应收敛,以双种群策略平衡探索与开发,引入哈里斯鹰围攻和缓存猎取算子提升寻优精度。然后,采用DGJO对时间卷积网络(temporal convolutional network,TCN)和双向门控循环单元(bidirectional gated recurrent unit,BiGRU)的超参数进行优化,提升收敛速度和模型的泛化能力。最后算例结果表明,所提模型在复杂约束与扰动情景下有较好的鲁棒性,并有效降低了系统的综合成本。
