The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negativ...The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.展开更多
As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based ...As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.展开更多
基金support by the Ministry of Science and Technology under Grant No.MOST 108-2622-E-169-006-CC3.
文摘The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.
基金This work was supported by the National Natural Science Foundation of China(No.51877078)the State Key Laboratory of Smart Grid Protection and Operation Control Open Project(No.SGNR0000KJJS1907535)the Beijing Nova Program(No.Z201100006820106)。
文摘As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.
