考虑储能调峰-调频复合场景有助于提高储能在新能源电网中经济性和应用效果。提出一种考虑调峰调频数据驱动建模的储能容量优化配置方法。首先,基于传统调峰与调频模型的数学描述,构建多时间尺度调峰调频场景下的端到端映射规则;然后,...考虑储能调峰-调频复合场景有助于提高储能在新能源电网中经济性和应用效果。提出一种考虑调峰调频数据驱动建模的储能容量优化配置方法。首先,基于传统调峰与调频模型的数学描述,构建多时间尺度调峰调频场景下的端到端映射规则;然后,利用深度学习端到端的特性及出色的非线性映射能力,运用长短期记忆网络(Long short term memory,LSTM)-Transformer模型挖掘调峰与调频场景的数据特征以保证潮流计算精度和加快频率计算速度;最后,基于历史数据和K-Medoids聚类选取典型日集合并将数据驱动模型嵌入到储能容量优化配置模型中,以储能配置经济性为目标函数,考虑系统内的运行约束建立储能配置-运行双层优化模型。算例表明,相较于单一场景,多场景储能配置下的经济性提升了34.7%,并且以数据驱动辅助储能优化模型可有效提高计算速度和精度。展开更多
本文运用长短期记忆网络(LSTM)和变换器(Transformer )混合模型预测豆油期货价格。通过数据处理、模型构建、训练评估与结果分析,发现该模型能有效地捕捉价格序列特征,在测试集上展现出良好预测性能,在平均绝对误差(MAE),均方根误差(RMS...本文运用长短期记忆网络(LSTM)和变换器(Transformer )混合模型预测豆油期货价格。通过数据处理、模型构建、训练评估与结果分析,发现该模型能有效地捕捉价格序列特征,在测试集上展现出良好预测性能,在平均绝对误差(MAE),均方根误差(RMSE),平均绝对百分比误差(MAPE),相对均方根误差(RRMSE)等指标上表现优异,与单独的Transformer模型和LSTM模型预测相比,精确度有明显的提高。这表示该模型在期货价格预测领域具有一定的应用潜力。This paper uses a hybrid model of Long Short-Term Memory (LSTM) and Transformer to predict the futures price of soybean oil. Through data processing, model construction, training evaluation, and result analysis, it is found that this model can effectively capture the characteristics of the price sequence. It demonstrates excellent prediction performance on the test set and shows outstanding performance in indicators such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Relative Root Mean Square Error (RRMSE). Compared with the predictions of the individual LSTM model and Transformer model, the accuracy has been significantly improved. This indicates that this model has certain application potential in the field of futures price prediction.展开更多
Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challen...Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.展开更多
文摘考虑储能调峰-调频复合场景有助于提高储能在新能源电网中经济性和应用效果。提出一种考虑调峰调频数据驱动建模的储能容量优化配置方法。首先,基于传统调峰与调频模型的数学描述,构建多时间尺度调峰调频场景下的端到端映射规则;然后,利用深度学习端到端的特性及出色的非线性映射能力,运用长短期记忆网络(Long short term memory,LSTM)-Transformer模型挖掘调峰与调频场景的数据特征以保证潮流计算精度和加快频率计算速度;最后,基于历史数据和K-Medoids聚类选取典型日集合并将数据驱动模型嵌入到储能容量优化配置模型中,以储能配置经济性为目标函数,考虑系统内的运行约束建立储能配置-运行双层优化模型。算例表明,相较于单一场景,多场景储能配置下的经济性提升了34.7%,并且以数据驱动辅助储能优化模型可有效提高计算速度和精度。
文摘本文运用长短期记忆网络(LSTM)和变换器(Transformer )混合模型预测豆油期货价格。通过数据处理、模型构建、训练评估与结果分析,发现该模型能有效地捕捉价格序列特征,在测试集上展现出良好预测性能,在平均绝对误差(MAE),均方根误差(RMSE),平均绝对百分比误差(MAPE),相对均方根误差(RRMSE)等指标上表现优异,与单独的Transformer模型和LSTM模型预测相比,精确度有明显的提高。这表示该模型在期货价格预测领域具有一定的应用潜力。This paper uses a hybrid model of Long Short-Term Memory (LSTM) and Transformer to predict the futures price of soybean oil. Through data processing, model construction, training evaluation, and result analysis, it is found that this model can effectively capture the characteristics of the price sequence. It demonstrates excellent prediction performance on the test set and shows outstanding performance in indicators such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Relative Root Mean Square Error (RRMSE). Compared with the predictions of the individual LSTM model and Transformer model, the accuracy has been significantly improved. This indicates that this model has certain application potential in the field of futures price prediction.
基金supported by the Science and Technology Project of Jiangsu Coastal Power Infrastructure Intelligent Engineering Research Center“Photovoltaic Power Prediction System Driven by Deep Learning and Multi-Source Data Fusion”(F2024-5044).
文摘Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.
