The creation of a suitable wide area monitoring system(WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure,efficient and sustainable for the foreseeable future. In Great ...The creation of a suitable wide area monitoring system(WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure,efficient and sustainable for the foreseeable future. In Great Britain(GB), the deployment of the first WAMS to monitor the entire power system in real time was the responsibility of the visualization of real time system dynamics using enhanced monitoring(VISOR) project. The core scope of the VISOR project is to deploy this WAMS and demonstrate how WAMS applications can in the near term provide system operators and planners with clear, actionable information. This paper presents the wider scope of the VISOR project and the GB wide WAMS that has been deployed. Furthermore, the paper describes some of the WAMS applications that have been deployed and provides examples of the measurement device performance issues that have been encountered during the project.展开更多
Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-qui...Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.展开更多
This publication proposes the use of an elasto-capillarity-driven self-assembly for fabricating a microscale merged-tip structure out of a variety of biocompatible UV-curable polymers for use as a microneedle platform...This publication proposes the use of an elasto-capillarity-driven self-assembly for fabricating a microscale merged-tip structure out of a variety of biocompatible UV-curable polymers for use as a microneedle platform.In addition,the novel merged-tip microstructure constitutes a new class of microneedles,which incorporates the convergence of biocompatible polymer micropillars,leading to the formation of a sharp tip and an open cavity capable of both liquid trapping and volume control.When combined with biocompatible photopolymer micropillar arrays fabricated with photolithography,elasto-capillarity-driven self-assembly provides a means for producing a complex microneedle-like structure without the use of micromolding or micromachining.This publication also explores and defines the design rules by which several fabrication aspects,such as micropillar dimensions,shapes,pattern array configurations,and materials,can be manipulated to produce a customizable microneedle array with controllable cavity volumes,fracture points,and merge profiles.In addition,the incorporation of a modular through-hole micropore membrane base was also investigated as a method for constitutive payload delivery and fluid-sampling functionalities.The flexibility and fabrication simplicity of the merged-tip microneedle platform holds promise in transdermal drug delivery applications.展开更多
The emerging medium voltage direct current(MVDC)distribution networks are becoming more attractive due to their flexible power flow control and lower losses compared to traditional AC networks.This will significantly ...The emerging medium voltage direct current(MVDC)distribution networks are becoming more attractive due to their flexible power flow control and lower losses compared to traditional AC networks.This will significantly increase the wide uptake of renewable energy sources.The optimum utilization of the existing assets is an important aspect in grid upgrading and planning.One feasible option is to convert existing MVAC lines into MVDC operation.One of the practical demonstrations is the“ANGLE-DC”project which is also the first MVDC link in the UK.This paper highlights the innovative approach,challenges and key benefits delivered by the ANGLE-DC project.展开更多
Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and ...Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.展开更多
基金supported by the GB Network Innovation Competition(NIC)
文摘The creation of a suitable wide area monitoring system(WAMS) is widely recognized as an essential aspect of delivering a power system that will be secure,efficient and sustainable for the foreseeable future. In Great Britain(GB), the deployment of the first WAMS to monitor the entire power system in real time was the responsibility of the visualization of real time system dynamics using enhanced monitoring(VISOR) project. The core scope of the VISOR project is to deploy this WAMS and demonstrate how WAMS applications can in the near term provide system operators and planners with clear, actionable information. This paper presents the wider scope of the VISOR project and the GB wide WAMS that has been deployed. Furthermore, the paper describes some of the WAMS applications that have been deployed and provides examples of the measurement device performance issues that have been encountered during the project.
文摘Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.
基金This work was supported by the National Research Foundation and funded by the Ministry of Education of Korea(NRF-2018R1A2A1A05019550,NRF-2016M3A9B4917321,and NRF-2016R1A4A1010796)the Korean Technology R&D Project,Ministry of Health&Welfare,Republic of Korea(HN14C0090).
文摘This publication proposes the use of an elasto-capillarity-driven self-assembly for fabricating a microscale merged-tip structure out of a variety of biocompatible UV-curable polymers for use as a microneedle platform.In addition,the novel merged-tip microstructure constitutes a new class of microneedles,which incorporates the convergence of biocompatible polymer micropillars,leading to the formation of a sharp tip and an open cavity capable of both liquid trapping and volume control.When combined with biocompatible photopolymer micropillar arrays fabricated with photolithography,elasto-capillarity-driven self-assembly provides a means for producing a complex microneedle-like structure without the use of micromolding or micromachining.This publication also explores and defines the design rules by which several fabrication aspects,such as micropillar dimensions,shapes,pattern array configurations,and materials,can be manipulated to produce a customizable microneedle array with controllable cavity volumes,fracture points,and merge profiles.In addition,the incorporation of a modular through-hole micropore membrane base was also investigated as a method for constitutive payload delivery and fluid-sampling functionalities.The flexibility and fabrication simplicity of the merged-tip microneedle platform holds promise in transdermal drug delivery applications.
基金ANGLE-DC,The Office of Gas and Electricity Markets(Ofgem)of UK Government funded Network Innovation Competition(NIC)project.
文摘The emerging medium voltage direct current(MVDC)distribution networks are becoming more attractive due to their flexible power flow control and lower losses compared to traditional AC networks.This will significantly increase the wide uptake of renewable energy sources.The optimum utilization of the existing assets is an important aspect in grid upgrading and planning.One feasible option is to convert existing MVAC lines into MVDC operation.One of the practical demonstrations is the“ANGLE-DC”project which is also the first MVDC link in the UK.This paper highlights the innovative approach,challenges and key benefits delivered by the ANGLE-DC project.
文摘Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.