Nd1-xSrxMnO3 （x ： 0.3, 0.5） ceramics containing a secondary phase are synthesized by high-energy ball milling and post heat-treatment method. The 4-wire and 2-wire measuring modes are used to investigate the transp...Nd1-xSrxMnO3 （x ： 0.3, 0.5） ceramics containing a secondary phase are synthesized by high-energy ball milling and post heat-treatment method. The 4-wire and 2-wire measuring modes are used to investigate the transport character of the grain/phase boundary （inner interface） and electrode-bulk interface （outer interface）, respectively, and the results indicate that there is a similar nonlinear I-V behaviour for both of the inner and outer interfaces, however, the electric pulse induced resistance change （EPIR） effect can only be observed at the outer interface.展开更多
With the depletion of fossil fuel and climate change,multi-energy systems have attracted widespread attention in buildings.Multi-energy systems,fuelled by renewable energy,including solar and biomass energy,are gain-i...With the depletion of fossil fuel and climate change,multi-energy systems have attracted widespread attention in buildings.Multi-energy systems,fuelled by renewable energy,including solar and biomass energy,are gain-ing increasing adoption in commercial buildings.Most of previous capacity design approaches are formulated based upon conventional operating schedules,which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system.Therefore,a two-stage capacity optimization approach is pro-posed for the multi-energy system with its optimal operating schedule taken into consideration.To demonstrate the effectiveness of the proposed capacity optimization approach,it is tested on a renewable energy fuelled multi-energy system in a commercial building.The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit,heat recovery unit,heat exchanger,absorption chiller,elec-tric chiller,biomass boiler,building integrated photovoltaic and photovoltaic thermal hybrid solar collector.The variable efficiency owing to weather condition and part-load operation is also considered.Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization,respectively.The two optimization stages are interrelated;thus,the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively.With the adoption of the proposed novel capacity optimization approach,there is a 14%reduction of year-round biomass consumption compared to one with the conventional capacity design approach.展开更多
A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United King...A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom.Due to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction accuracy.The architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting weights.The optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious task.To address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization ability.One year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,respectively.To demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network model.The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.展开更多
基金the Project of Hubei Polytechnic University (No.12xjz01R)The Natural Science Foundation of Hubei Province(No.2012FFB01001)the Program of Ministry of Education of China(for New Century Excellent Talents in University, No.NCET-08-0674)for their financial supports
文摘Nd1-xSrxMnO3 （x ： 0.3, 0.5） ceramics containing a secondary phase are synthesized by high-energy ball milling and post heat-treatment method. The 4-wire and 2-wire measuring modes are used to investigate the transport character of the grain/phase boundary （inner interface） and electrode-bulk interface （outer interface）, respectively, and the results indicate that there is a similar nonlinear I-V behaviour for both of the inner and outer interfaces, however, the electric pulse induced resistance change （EPIR） effect can only be observed at the outer interface.
文摘With the depletion of fossil fuel and climate change,multi-energy systems have attracted widespread attention in buildings.Multi-energy systems,fuelled by renewable energy,including solar and biomass energy,are gain-ing increasing adoption in commercial buildings.Most of previous capacity design approaches are formulated based upon conventional operating schedules,which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system.Therefore,a two-stage capacity optimization approach is pro-posed for the multi-energy system with its optimal operating schedule taken into consideration.To demonstrate the effectiveness of the proposed capacity optimization approach,it is tested on a renewable energy fuelled multi-energy system in a commercial building.The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit,heat recovery unit,heat exchanger,absorption chiller,elec-tric chiller,biomass boiler,building integrated photovoltaic and photovoltaic thermal hybrid solar collector.The variable efficiency owing to weather condition and part-load operation is also considered.Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization,respectively.The two optimization stages are interrelated;thus,the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively.With the adoption of the proposed novel capacity optimization approach,there is a 14%reduction of year-round biomass consumption compared to one with the conventional capacity design approach.
文摘A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom.Due to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction accuracy.The architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting weights.The optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious task.To address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization ability.One year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,respectively.To demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network model.The comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.
