Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a ...Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.展开更多
To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided ...To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.展开更多
为提高综合能源系统(integrated energy system,IES)多元负荷预测的精确度,综合考虑多能源相互作用机理、多元负荷耦合特性及气象因素相关性,提出了一种基于多尺度特征提取的IES多元负荷短期联合预测方法。首先,通过最大互信息系数(maxi...为提高综合能源系统(integrated energy system,IES)多元负荷预测的精确度,综合考虑多能源相互作用机理、多元负荷耦合特性及气象因素相关性,提出了一种基于多尺度特征提取的IES多元负荷短期联合预测方法。首先,通过最大互信息系数(maximum information coefficient,MIC)研究多元负荷耦合特性及影响因素相关性,选择预测特征;其次,利用变分模态分解技术(variational mode decomposition,VMD)对输入特征进行分解,提升特征纯洁度;最后,采用卷积神经网络-双向长短期记忆神经网络(convolutional neural network-bidirectional long and short-term memory,CNN-BiLSTM)多任务学习模型进行纵向、横向特征选择,注意力(Attention)机制对重要特征差异化提取,实现多尺度特征提取,并利用雪消融优化器(snow ablation optmizer,SAO)对VMD和CNN-BiLSTM多任务学习模型进行超参数优化,以此实现IES多元负荷的联合预测。以美国亚利桑那州实测数据进行实验,结果表明,无论与单一预测方法还是与其他模型相比,所提联合预测方法的均方根误差更低、准确率更高,在IES多元负荷预测中具有更高的精确性和鲁棒性。展开更多
文摘Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.
文摘To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.
文摘为提高综合能源系统(integrated energy system,IES)多元负荷预测的精确度,综合考虑多能源相互作用机理、多元负荷耦合特性及气象因素相关性,提出了一种基于多尺度特征提取的IES多元负荷短期联合预测方法。首先,通过最大互信息系数(maximum information coefficient,MIC)研究多元负荷耦合特性及影响因素相关性,选择预测特征;其次,利用变分模态分解技术(variational mode decomposition,VMD)对输入特征进行分解,提升特征纯洁度;最后,采用卷积神经网络-双向长短期记忆神经网络(convolutional neural network-bidirectional long and short-term memory,CNN-BiLSTM)多任务学习模型进行纵向、横向特征选择,注意力(Attention)机制对重要特征差异化提取,实现多尺度特征提取,并利用雪消融优化器(snow ablation optmizer,SAO)对VMD和CNN-BiLSTM多任务学习模型进行超参数优化,以此实现IES多元负荷的联合预测。以美国亚利桑那州实测数据进行实验,结果表明,无论与单一预测方法还是与其他模型相比,所提联合预测方法的均方根误差更低、准确率更高,在IES多元负荷预测中具有更高的精确性和鲁棒性。
