In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis ...In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis of the data during 1992～1998, and the approximate load in 2010 is forecast on the basis of the data during 1990～1998.展开更多
Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundan...Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.展开更多
The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based earl...The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now-and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5–6 December, 2013(German name ‘Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the Eur Otop(2016) approach.展开更多
Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration...Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.展开更多
A systematic study on the electrical load forecasting for large-scale iron and steel companies was made. After analyzing the electrical load＇s characteristics, an algorithm framework for the load forecasting in iron ...A systematic study on the electrical load forecasting for large-scale iron and steel companies was made. After analyzing the electrical load＇s characteristics, an algorithm framework for the load forecasting in iron and steel complex was formulated based on model combination and scheme filtration. The algorithm features data quality self- adaptation, convenient forecasting model extension, easy practical application, etc. , and has been successfully applied in Baoshan Iron and Steel Co Ltd, Shanghai, China, resulting in great economic benefit.展开更多
With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distri...With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.展开更多
超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local...超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。展开更多
负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单...负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单一模型的不足,提出一种基于TCN-TPABiLSTM组合模型和多任务学习框架的IES多元负荷超短期协同预测方法。首先对负荷间耦合相关性、负荷时间相关性和负荷影响因素进行分析以构建模型输入,再通过变分模态分解将负荷数据分解为一定数量的模态以降低非平稳性,最后以TCN-TPA-BiLSTM组合模型作为多任务学习框架的共享层进行预测。通过实际数据进行验证和对比,结果表明该方法能够充分发挥模型各部分优势,相较于其他模型也获得了更优的结果。展开更多
电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负...电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负荷的影响。为了提高EV充电站短期充电负荷预测精度,提出一种冠豪猪优化器变分模态分解双向长短期记忆神经网络(crested porcupine optimizer variational mode decomposition bidirectional long short term memory,CPO VMD BiLSTM)组合模型进行EV充电站短期充电负荷预测的方法。首先,考虑影响EV充电负荷的多种因素和历史充电站充电负荷共同构成输入特征矩阵。然后利用CPO算法对VMD其核心参数进行优化搜索,实现参数自适应优化设置。之后采用CPO VMD对历史充电负荷数据进行分解,弱化负荷的非平稳性,捕捉其局部特征。最后在BiLSTM模型中输入分解后的特征矩阵来实现充电站短期充电负荷的预测目标。以美国ANN DATA公开数据集中位于加州理工大学校园内EV充电站的历史充电负荷数据作为实际算例,与独立模型、未优化组合模型、优化组合模型进行对比,均方根误差(root mean squared error,RMSE)和平均绝对误差(mean absolute error,MAE)平均降低了41.23%和59.04%。因此,验证了提出方法在充电站充电负荷短期预测中精度的提高和实用性。展开更多
文摘In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis of the data during 1992～1998, and the approximate load in 2010 is forecast on the basis of the data during 1990～1998.
文摘Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.
基金the joint research project Early Dike–Sensor and Risk based Early Warning Systems for Coastal Dikes(No.03G0847C)funded by the German Ministry of Education and Research(BMBF)
文摘The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now-and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5–6 December, 2013(German name ‘Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the Eur Otop(2016) approach.
文摘Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.
基金Item Sponsored by National Natural Science Foundation of China (59937150 ,60274054),863 High Tech Development Plan ofChina (2001AA413910) and National Outstanding Young Investigator Grant (6970025)
文摘A systematic study on the electrical load forecasting for large-scale iron and steel companies was made. After analyzing the electrical load＇s characteristics, an algorithm framework for the load forecasting in iron and steel complex was formulated based on model combination and scheme filtration. The algorithm features data quality self- adaptation, convenient forecasting model extension, easy practical application, etc. , and has been successfully applied in Baoshan Iron and Steel Co Ltd, Shanghai, China, resulting in great economic benefit.
文摘With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.
文摘超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。
文摘负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单一模型的不足,提出一种基于TCN-TPABiLSTM组合模型和多任务学习框架的IES多元负荷超短期协同预测方法。首先对负荷间耦合相关性、负荷时间相关性和负荷影响因素进行分析以构建模型输入,再通过变分模态分解将负荷数据分解为一定数量的模态以降低非平稳性,最后以TCN-TPA-BiLSTM组合模型作为多任务学习框架的共享层进行预测。通过实际数据进行验证和对比,结果表明该方法能够充分发挥模型各部分优势,相较于其他模型也获得了更优的结果。
文摘电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负荷的影响。为了提高EV充电站短期充电负荷预测精度,提出一种冠豪猪优化器变分模态分解双向长短期记忆神经网络(crested porcupine optimizer variational mode decomposition bidirectional long short term memory,CPO VMD BiLSTM)组合模型进行EV充电站短期充电负荷预测的方法。首先,考虑影响EV充电负荷的多种因素和历史充电站充电负荷共同构成输入特征矩阵。然后利用CPO算法对VMD其核心参数进行优化搜索,实现参数自适应优化设置。之后采用CPO VMD对历史充电负荷数据进行分解,弱化负荷的非平稳性,捕捉其局部特征。最后在BiLSTM模型中输入分解后的特征矩阵来实现充电站短期充电负荷的预测目标。以美国ANN DATA公开数据集中位于加州理工大学校园内EV充电站的历史充电负荷数据作为实际算例,与独立模型、未优化组合模型、优化组合模型进行对比,均方根误差(root mean squared error,RMSE)和平均绝对误差(mean absolute error,MAE)平均降低了41.23%和59.04%。因此,验证了提出方法在充电站充电负荷短期预测中精度的提高和实用性。
