本文旨在讨论核能5.0(Nuclear Energy 5.0)的基本概念、体系架构和关键平台技术等问题.首先讨论了核能5.0出现的新智能时代基础,阐述了虚拟数字工业崛起的技术背景.详细叙述了核电工业新形态与体系结构,即平行核能的定义、意义、研究内...本文旨在讨论核能5.0(Nuclear Energy 5.0)的基本概念、体系架构和关键平台技术等问题.首先讨论了核能5.0出现的新智能时代基础,阐述了虚拟数字工业崛起的技术背景.详细叙述了核电工业新形态与体系结构,即平行核能的定义、意义、研究内容、体系架构以及应用领域.接下来讨论了核能5.0中新一代核心技术,包括核能物联网、知识自动化、发展性人工智能、大规模协同演进技术、核能区块链等.最后讨论了核能5.0中在核电系统的具体应用场景与案例,重点是核电工控系统安全评估与核电站数字化仪控系统.展开更多
Concern about global warming calls for an advanced approach for designing an energy system to reduce carbon emissions as well as to secure energy security for each country.Conventional energy systems tend to introduce...Concern about global warming calls for an advanced approach for designing an energy system to reduce carbon emissions as well as to secure energy security for each country.Conventional energy systems tend to introduce different technologies with high conversion efficiency,leading to a higher average efficiency.Advanced energy systems can be achieved not by an aggregate form of conversion technologies but by an innovative system design itself.The concept of LCS(low carbon society) is a unique approach having multi-dimensional considerations such as social,economic and environmental dimensions.The LCS aims at an extensive restructuring of worldwide energy supply/demand network system by not only replacing the conventional parts with the new ones,but also integrating all the necessary components and designing absolutely different energy networks.As a core tool for the LCS design,energy-economic models are applied to show feasible solutions in future with alternatives such as renewable resources,combined heat and power,and smart grid operations.Models can introduce changes in energy markets,technology learning in capacity,and penetration of innovative technologies,leading to an optimum system configuration under priority settings.The paper describes recent trials of energy models application related to waste-to-energy,clean coal,transportation and rural development.Although the modelling approach is still under investigation,the output clearly shows possible options having variety of technologies and linkages between supply and demand sides.Design of the LCS means an energy systems design with the modelling approach,which gives solution for complex systems,choices among technologies,technology feasibility,R&D targets,and what we need to start.展开更多
Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility.In recent years,the data-driven approach based on machine learning algori...Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility.In recent years,the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems(BASs),which automatically collect and store real-time building operational data.For new buildings and most existing buildings without installing advanced BASs,there is a lack of sufficient data to train data-driven predictive models.Transfer learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other buildings.Few studies focused on the influences of source building datasets,pre-training data volume,and training data volume on the performance of the transfer learning method.The present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research gap.Around 400 non-residential buildings’data from the open-source Building Genome Project are used to test the proposed method.Extensive analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training data.The most influential building features which influence the effectiveness of transfer learning are found to be building usage and industry.The research outcomes can provide guidance for implementation of transfer learning,especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.展开更多
文摘本文旨在讨论核能5.0(Nuclear Energy 5.0)的基本概念、体系架构和关键平台技术等问题.首先讨论了核能5.0出现的新智能时代基础,阐述了虚拟数字工业崛起的技术背景.详细叙述了核电工业新形态与体系结构,即平行核能的定义、意义、研究内容、体系架构以及应用领域.接下来讨论了核能5.0中新一代核心技术,包括核能物联网、知识自动化、发展性人工智能、大规模协同演进技术、核能区块链等.最后讨论了核能5.0中在核电系统的具体应用场景与案例,重点是核电工控系统安全评估与核电站数字化仪控系统.
文摘Concern about global warming calls for an advanced approach for designing an energy system to reduce carbon emissions as well as to secure energy security for each country.Conventional energy systems tend to introduce different technologies with high conversion efficiency,leading to a higher average efficiency.Advanced energy systems can be achieved not by an aggregate form of conversion technologies but by an innovative system design itself.The concept of LCS(low carbon society) is a unique approach having multi-dimensional considerations such as social,economic and environmental dimensions.The LCS aims at an extensive restructuring of worldwide energy supply/demand network system by not only replacing the conventional parts with the new ones,but also integrating all the necessary components and designing absolutely different energy networks.As a core tool for the LCS design,energy-economic models are applied to show feasible solutions in future with alternatives such as renewable resources,combined heat and power,and smart grid operations.Models can introduce changes in energy markets,technology learning in capacity,and penetration of innovative technologies,leading to an optimum system configuration under priority settings.The paper describes recent trials of energy models application related to waste-to-energy,clean coal,transportation and rural development.Although the modelling approach is still under investigation,the output clearly shows possible options having variety of technologies and linkages between supply and demand sides.Design of the LCS means an energy systems design with the modelling approach,which gives solution for complex systems,choices among technologies,technology feasibility,R&D targets,and what we need to start.
基金The authors gratefully acknowledge the support of this research by the Research Grant Council of the Hong Kong SAR(152133/19E).
文摘Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility.In recent years,the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems(BASs),which automatically collect and store real-time building operational data.For new buildings and most existing buildings without installing advanced BASs,there is a lack of sufficient data to train data-driven predictive models.Transfer learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other buildings.Few studies focused on the influences of source building datasets,pre-training data volume,and training data volume on the performance of the transfer learning method.The present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research gap.Around 400 non-residential buildings’data from the open-source Building Genome Project are used to test the proposed method.Extensive analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training data.The most influential building features which influence the effectiveness of transfer learning are found to be building usage and industry.The research outcomes can provide guidance for implementation of transfer learning,especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.
