Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a s...Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.展开更多
Photovoltaics,energy storage,direct current and flexibility(PEDF)are important pillars of achievement on the path to manufacturing nearly zero energy buildings(NZEBs).HVAC systems,which are an important part of public...Photovoltaics,energy storage,direct current and flexibility(PEDF)are important pillars of achievement on the path to manufacturing nearly zero energy buildings(NZEBs).HVAC systems,which are an important part of public buildings,play a key role in adapting to PDEF systems.This research studied the basic principles and operational control strategies of a DC inverter heat pump using a DC distribution network with the aim of contributing to the development and application of small DC distribution systems.Along with the characteristics of a DC distribution network and different operating conditions,a DC inverter heat pump has the ability to adapt to changes in the DC bus voltage and adds flexibility to the system.Theoretical models of the DC inverter heat pump integrated with an ice storage unit were developed.The control strategies of the DC inverter heat pump system considered the influence of both room temperature and varied bus voltage.A simulation study was conducted using MATLAB&Simulink software with simulation results validated by experimental data.The results showed that:(1)The bus fluctuation under the rated working voltage had little effect on the operation of the unit;(2)When the bus voltage was fluctuating from 80%-90%or 105%-107%,the heat pump could still operate normally by reducing the frequency;(3)When the bus voltage was less than 80%or more than 107%,the unit needed to be shut down for the sake of equipment safety,so that the energy storage device could adjust to the sharp decrease or rise of voltage.展开更多
The magnetocaloric effect of Mn,Ni,and Mn-Ni-doped EuTiO3 compounds are studied in the near-liquid-helium-temperature range.The Eu(Ti0.9375Mn0.0625)O3,Eu(Ti0.975Ni0.025)O3,and Eu(Ti0.9125Mn0.0625Ni0.025)O3 are prepare...The magnetocaloric effect of Mn,Ni,and Mn-Ni-doped EuTiO3 compounds are studied in the near-liquid-helium-temperature range.The Eu(Ti0.9375Mn0.0625)O3,Eu(Ti0.975Ni0.025)O3,and Eu(Ti0.9125Mn0.0625Ni0.025)O3 are prepared by the sol-gel method.The Eu(Ti0.9375Mn0.0625)O3 and Eu(Ti0.9125Mn0.0625Ni0.025)O3 exhibit ferromagnetism with second-order phase transition,and the Eu(Ti0.975Ni0.025)O3 displays antiferromagnetic behavior.Under the magnetic field change of 10 kOe(1 Oe=79.5775 Am-1),the values of magnetic entropy change are 8.8 Jkg-1K-1,12 Jkg-1K-1,and 10.9 Jkg-1K-1 for Eu(Ti0.9375Mn0.0625)O3,Eu(Ti0.975Ni0.025)O3,and Eu(Ti0.9125Mn0.0625Ni0.025)O3,respectively.The co-substitution of Mn and Ni can not only improve the magnetic entropy change,but also widen the refrigeration temperature window,which greatly enhances the magnetic refrigeration capacity.Under the magnetic field change of 10 kOe,the refrigerant capacity value of Eu(Ti0.9125Mn0.0625Ni0.025)O3 is 62.6 Jkg-1 more than twice that of EuTiO3(27 Jkg-1),indicating that multi-component substitution can lead to better magnetocaloric performance.展开更多
Pyroptosis,a unique lytic programmed cell death,inspired tempting implications as potent anti-tumor strategy in pertinent to its potentials in stimulating anti-tumor immunity for eradication of primary tumors and meta...Pyroptosis,a unique lytic programmed cell death,inspired tempting implications as potent anti-tumor strategy in pertinent to its potentials in stimulating anti-tumor immunity for eradication of primary tumors and metastasis.Nonetheless,rare therapeutics have been reported to successfully stimulate pyroptosis.In view of the intimate participation of reactive oxygen species(ROS)in stimulating pyroptosis,we attempted to devise a spectrum of well-defined subcellular organelle(including mitochondria,lysosomes and endoplasmic reticulum)-targeting photosensitizers with the aim of precisely localizing ROS(produced from photosensitizers)at the subcellular compartments and explore their potentials in urging pyroptosis and immunogenic cell death(ICD).The subsequent investigations revealed varied degrees of pyroptosis upon photodynamic therapy(PDT)towards cancerous cells,as supported by not only observation of the distinctive morphological and mechanistic characteristics of pyroptosis,but for the first-time explicit validation from comprehensive RNA-Seq analysis.Furthermore,in vivo anti-tumor PDT could exert eradication of the primary tumors,more importantly suppressed the distant tumor and metastatic tumor growth through an abscopal effect,approving the acquirement of specific anti-tumor immunity as a consequence of pyroptosis.Hence,pyroptosis was concluded unprecedently by our proposed organelles-targeting PDT strategy and explicitly delineated with molecular insights into its occurrence and the consequent ICD.展开更多
Li-ion batteries are widely used in electric vehicles(EVs).However,the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries.This paper proposes a decoupling multiple f...Li-ion batteries are widely used in electric vehicles(EVs).However,the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries.This paper proposes a decoupling multiple forgetting factors recursive least squares method(DMFFRLS)for EV battery parameter identification.The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters.The Thevenin equivalent circuit model(ECM)is employed considering the complexity of battery management system(BMS)on the basis of comparative analysis of several common battery ECMs.In addition,decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process.Numerous experiments are employed to verify the proposed DMFFRLS method.The parameters for commonly used LiFePO4(LFP),Li(NiCoMn)O2(NCM)battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods.The experimental results show that the error of the DMFFRLS method is less than 15 mV,which is significantly lower than the conventional methods.The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries,and provides a basis for state of charge(SOC)estimation and BMS design of EVs.展开更多
基金the Shanghai Rising-Star Program(No.22QA1403900)the National Natural Science Foundation of China(No.71804106)the Noncarbon Energy Conversion and Utilization Institute under the Shanghai Class IV Peak Disciplinary Development Program.
文摘Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.
基金funded by State Grid Science&Technology Project“Research and Demonstration of Key Technologies on Electric-Heating Collaboration Cross-Network Mutual Supply for Typical Regional Clean Energy”,Grant Number 5400-202111575A-0-5-SF.
文摘Photovoltaics,energy storage,direct current and flexibility(PEDF)are important pillars of achievement on the path to manufacturing nearly zero energy buildings(NZEBs).HVAC systems,which are an important part of public buildings,play a key role in adapting to PDEF systems.This research studied the basic principles and operational control strategies of a DC inverter heat pump using a DC distribution network with the aim of contributing to the development and application of small DC distribution systems.Along with the characteristics of a DC distribution network and different operating conditions,a DC inverter heat pump has the ability to adapt to changes in the DC bus voltage and adds flexibility to the system.Theoretical models of the DC inverter heat pump integrated with an ice storage unit were developed.The control strategies of the DC inverter heat pump system considered the influence of both room temperature and varied bus voltage.A simulation study was conducted using MATLAB&Simulink software with simulation results validated by experimental data.The results showed that:(1)The bus fluctuation under the rated working voltage had little effect on the operation of the unit;(2)When the bus voltage was fluctuating from 80%-90%or 105%-107%,the heat pump could still operate normally by reducing the frequency;(3)When the bus voltage was less than 80%or more than 107%,the unit needed to be shut down for the sake of equipment safety,so that the energy storage device could adjust to the sharp decrease or rise of voltage.
基金Project supported by the National Key Research and Development Program of China(Grant No.2017YFB0702704)the National Natural Science Foundation of China(Grant Nos.11504266 and 51676198)+1 种基金the Tianjin Natural Science Foundation,China(Grant No.17JCQNJC02300)the Science&Technology Development Fund of Tianjin Education Commission for Higher Education,China(Grant No.2017KJ247).
文摘The magnetocaloric effect of Mn,Ni,and Mn-Ni-doped EuTiO3 compounds are studied in the near-liquid-helium-temperature range.The Eu(Ti0.9375Mn0.0625)O3,Eu(Ti0.975Ni0.025)O3,and Eu(Ti0.9125Mn0.0625Ni0.025)O3 are prepared by the sol-gel method.The Eu(Ti0.9375Mn0.0625)O3 and Eu(Ti0.9125Mn0.0625Ni0.025)O3 exhibit ferromagnetism with second-order phase transition,and the Eu(Ti0.975Ni0.025)O3 displays antiferromagnetic behavior.Under the magnetic field change of 10 kOe(1 Oe=79.5775 Am-1),the values of magnetic entropy change are 8.8 Jkg-1K-1,12 Jkg-1K-1,and 10.9 Jkg-1K-1 for Eu(Ti0.9375Mn0.0625)O3,Eu(Ti0.975Ni0.025)O3,and Eu(Ti0.9125Mn0.0625Ni0.025)O3,respectively.The co-substitution of Mn and Ni can not only improve the magnetic entropy change,but also widen the refrigeration temperature window,which greatly enhances the magnetic refrigeration capacity.Under the magnetic field change of 10 kOe,the refrigerant capacity value of Eu(Ti0.9125Mn0.0625Ni0.025)O3 is 62.6 Jkg-1 more than twice that of EuTiO3(27 Jkg-1),indicating that multi-component substitution can lead to better magnetocaloric performance.
基金This research was partially funded by National Natural Science Foundation of China(No.22078050)Fundamental Research Funds for the Central Universities[No.DUT17RC(3)059,DUT20YG126]+1 种基金Dalian Science&Technology Innovation Fund(2020JJ26SN050,2020JJ26GX025)Talent Project of Revitalizing Liaoning(XLYC1807184).
文摘Pyroptosis,a unique lytic programmed cell death,inspired tempting implications as potent anti-tumor strategy in pertinent to its potentials in stimulating anti-tumor immunity for eradication of primary tumors and metastasis.Nonetheless,rare therapeutics have been reported to successfully stimulate pyroptosis.In view of the intimate participation of reactive oxygen species(ROS)in stimulating pyroptosis,we attempted to devise a spectrum of well-defined subcellular organelle(including mitochondria,lysosomes and endoplasmic reticulum)-targeting photosensitizers with the aim of precisely localizing ROS(produced from photosensitizers)at the subcellular compartments and explore their potentials in urging pyroptosis and immunogenic cell death(ICD).The subsequent investigations revealed varied degrees of pyroptosis upon photodynamic therapy(PDT)towards cancerous cells,as supported by not only observation of the distinctive morphological and mechanistic characteristics of pyroptosis,but for the first-time explicit validation from comprehensive RNA-Seq analysis.Furthermore,in vivo anti-tumor PDT could exert eradication of the primary tumors,more importantly suppressed the distant tumor and metastatic tumor growth through an abscopal effect,approving the acquirement of specific anti-tumor immunity as a consequence of pyroptosis.Hence,pyroptosis was concluded unprecedently by our proposed organelles-targeting PDT strategy and explicitly delineated with molecular insights into its occurrence and the consequent ICD.
基金This work was supported by Science and Technology Project of State Grid Corporation of China(5202011600U5).
文摘Li-ion batteries are widely used in electric vehicles(EVs).However,the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries.This paper proposes a decoupling multiple forgetting factors recursive least squares method(DMFFRLS)for EV battery parameter identification.The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters.The Thevenin equivalent circuit model(ECM)is employed considering the complexity of battery management system(BMS)on the basis of comparative analysis of several common battery ECMs.In addition,decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process.Numerous experiments are employed to verify the proposed DMFFRLS method.The parameters for commonly used LiFePO4(LFP),Li(NiCoMn)O2(NCM)battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods.The experimental results show that the error of the DMFFRLS method is less than 15 mV,which is significantly lower than the conventional methods.The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries,and provides a basis for state of charge(SOC)estimation and BMS design of EVs.
