Efficient built environment control is essential for balancing energy consumption,thermal comfort,and indoor air quality(IAQ),especially in spaces with highly dynamic and intermittent occupancy patterns.Traditional co...Efficient built environment control is essential for balancing energy consumption,thermal comfort,and indoor air quality(IAQ),especially in spaces with highly dynamic and intermittent occupancy patterns.Traditional control strategies,such as fixed schedules or simple occupancy-based rules,often fail to address the stochastic nature of occupancy behaviors,leading to suboptimal performance.This study proposes a stochastic occupancy-integrated model predictive control(MPC)strategy that advances built environment optimization through several innovative contributions.First,the proposed MPC integrates stochastic occupancy number predictions into its control scheme,enabling multi-objective optimization considering thermal comfort and IAQ for spaces with sudden occupancy changes and irregular usage.Second,the stochastic differential equations(SDE)-based building dynamic models are developed considering the stochasticity and time-inhomogeneity of occupancy heat gains and CO_(2)generations in the prediction of indoor temperature,CO_(2)concentration and energy consumption.Third,a TRNSYS-Python co-simulation platform is established to evaluate the MPC strategy’s performance,addressing the discrepancies between the SDE models used for MPC and the actual process of the target system.Finally,the study comprehensively evaluates the MPC’s multi-dimensional performance under different optimization weight combinations and benchmarks it against two baseline strategies:a fixed-schedule(FIX)strategy and occupancy-based control(OBC)strategies with varying per-person fresh airflow rates.Simulation results demonstrate that the proposed MPC achieves 32%energy savings and 17%IAQ improvement compared to the FIX strategy,and 30%thermal comfort improvement and 20%IAQ improvement with the same energy consumption compared to OBC.These findings highlight the robustness and enhanced performance of the proposed MPC in addressing the complexities of stochastic and time-varying occupancy,offering a state-of-the-art solution for energy-efficient and occupant-centric built environment control.展开更多
The mined-out areas formed by ore extraction have promoted the development of seasonal energy storage technology in underground spaces.Currently,most studies on the heat storage/release performance of backfills with e...The mined-out areas formed by ore extraction have promoted the development of seasonal energy storage technology in underground spaces.Currently,most studies on the heat storage/release performance of backfills with embedded heat exchange pipes have idealized the operating conditions,such as constant fluid inlet temperature and flow rate.However,actual operating conditions are influenced by many factors like weather conditions,surface equipment,and heat load fluctuations,making them unstable.Therefore,this paper constructs a solar-assisted heat pump coupled mine backfill body heat storage system(SAHP-MBBHSs)based on TRNSYS simulation software and verifies the accuracy of the backfill heat exchangers(BFHEs)model through experiments.Considering the influence of various external factors on the operating conditions,we investigated the long-term seasonal heat storage/release performance of the BFHEs,focusing on the effects of solar collector area,U-tube spacing,thermal conductivity of backfill materials,and heat storage start/stop time.The results show that reducing the U-tube spacing increases the fluctuation amplitude of the average temperature of the backfill body,with the maximum average fluctuation amplitude difference reaching 16.6℃between the 11th and 15th years.Delaying the onset of thermal storage reduces the storage effectiveness of the U-BFHEs,while increasing the heat release effectiveness.During the thermal storage/release interval,heat loss to the surrounding rock does not exceed 4.7%,with the minimal overall impact.The thermal conductivity of the backfill body has the greatest effect on the heat transfer effectiveness of U-BFHEs,increasing from 1 W·m^(-1)·K^(-1)to 2 W·m^(-1)·K^(-1)resulting in respective increases of 58.8%and 39.2%in the heat transfer effectiveness during the 15th year of thermal storage/release.The total heat storage-release effectiveness of the U-BFHEs does not exceed 43.7%,indicating significant room for improvement.Utilizing seasonal thermal storage in the backfill body can effectively enhance the heating performance of SAHP-MBBHSs,with the maximum average APF and HSPF values reaching 3.85 and 5.43,respectively,during the 11th-15th years of operation,maintaining high efficiency even after long-term operation.展开更多
In just one and half minutes,more than fifty thousand died due to the 7.7 and 7.6 magnitude earthquakes that struck Turkey’s southeast on February 6,2023;thousands of families who barely escaped struggled to survive ...In just one and half minutes,more than fifty thousand died due to the 7.7 and 7.6 magnitude earthquakes that struck Turkey’s southeast on February 6,2023;thousands of families who barely escaped struggled to survive in the freezing weather.A warm shelter was the most basic requirement of these families.Container buildings are a rapid and easy solution to this issue.However,there is a need for a more effective and safe heating option than a wood fire for these buildings.In this study,cabin heaters,which allow truck drivers to warm up when they park their vehicles to sleep,are specially optimized for emergency shelters after an earthquake.An optimized fuzzy controller was developed to use in such buildings,which allows an air–fuel ratio in the combustion chamber of the cabin heater to be controlled adaptively based on system dynamics to get lower carbon emissions and fuel consumption.The TRNSYS software was used to establish the transient simulation model of a cabin heater with a capacity of 4 kW for a typical 21 m^(2) shelter building in Turkey’s cold regions.The developed fuzzy controller carried out the heating process of this shelter from the 15th of November to the 15th of March.Instead of using expert knowledge,the Gray Wolf Optimization(GWO)method was applied to optimize the fuzzy controller parameters developed for the cabin heater.With the optimized fuzzy controller,the fuel consumption at the end of the heating season was reduced by an average of 0.2 L/h,and the cabin heater’s efficiency increased by more than 13%.Our simulation results show that the intelligent controller we developed could improve diesel fuel combustion efficiency by up to 85%.展开更多
基金the Research Grants Council(15220323)of the Hong Kong SAR,Chinathe Innovation Fund Denmark to SEM4Cities(IFD No.0143-0004)and RePUP(IFD No.2079-00030B)as well as the ARV project(EU H2020101036723).
文摘Efficient built environment control is essential for balancing energy consumption,thermal comfort,and indoor air quality(IAQ),especially in spaces with highly dynamic and intermittent occupancy patterns.Traditional control strategies,such as fixed schedules or simple occupancy-based rules,often fail to address the stochastic nature of occupancy behaviors,leading to suboptimal performance.This study proposes a stochastic occupancy-integrated model predictive control(MPC)strategy that advances built environment optimization through several innovative contributions.First,the proposed MPC integrates stochastic occupancy number predictions into its control scheme,enabling multi-objective optimization considering thermal comfort and IAQ for spaces with sudden occupancy changes and irregular usage.Second,the stochastic differential equations(SDE)-based building dynamic models are developed considering the stochasticity and time-inhomogeneity of occupancy heat gains and CO_(2)generations in the prediction of indoor temperature,CO_(2)concentration and energy consumption.Third,a TRNSYS-Python co-simulation platform is established to evaluate the MPC strategy’s performance,addressing the discrepancies between the SDE models used for MPC and the actual process of the target system.Finally,the study comprehensively evaluates the MPC’s multi-dimensional performance under different optimization weight combinations and benchmarks it against two baseline strategies:a fixed-schedule(FIX)strategy and occupancy-based control(OBC)strategies with varying per-person fresh airflow rates.Simulation results demonstrate that the proposed MPC achieves 32%energy savings and 17%IAQ improvement compared to the FIX strategy,and 30%thermal comfort improvement and 20%IAQ improvement with the same energy consumption compared to OBC.These findings highlight the robustness and enhanced performance of the proposed MPC in addressing the complexities of stochastic and time-varying occupancy,offering a state-of-the-art solution for energy-efficient and occupant-centric built environment control.
基金supported by National Natural Science Foundation of China(Nos.52274063,52104148,52004207,52074212)Natural Science Basic Research Plan of Shaanxi Province of China(No.2022JM-173)。
文摘The mined-out areas formed by ore extraction have promoted the development of seasonal energy storage technology in underground spaces.Currently,most studies on the heat storage/release performance of backfills with embedded heat exchange pipes have idealized the operating conditions,such as constant fluid inlet temperature and flow rate.However,actual operating conditions are influenced by many factors like weather conditions,surface equipment,and heat load fluctuations,making them unstable.Therefore,this paper constructs a solar-assisted heat pump coupled mine backfill body heat storage system(SAHP-MBBHSs)based on TRNSYS simulation software and verifies the accuracy of the backfill heat exchangers(BFHEs)model through experiments.Considering the influence of various external factors on the operating conditions,we investigated the long-term seasonal heat storage/release performance of the BFHEs,focusing on the effects of solar collector area,U-tube spacing,thermal conductivity of backfill materials,and heat storage start/stop time.The results show that reducing the U-tube spacing increases the fluctuation amplitude of the average temperature of the backfill body,with the maximum average fluctuation amplitude difference reaching 16.6℃between the 11th and 15th years.Delaying the onset of thermal storage reduces the storage effectiveness of the U-BFHEs,while increasing the heat release effectiveness.During the thermal storage/release interval,heat loss to the surrounding rock does not exceed 4.7%,with the minimal overall impact.The thermal conductivity of the backfill body has the greatest effect on the heat transfer effectiveness of U-BFHEs,increasing from 1 W·m^(-1)·K^(-1)to 2 W·m^(-1)·K^(-1)resulting in respective increases of 58.8%and 39.2%in the heat transfer effectiveness during the 15th year of thermal storage/release.The total heat storage-release effectiveness of the U-BFHEs does not exceed 43.7%,indicating significant room for improvement.Utilizing seasonal thermal storage in the backfill body can effectively enhance the heating performance of SAHP-MBBHSs,with the maximum average APF and HSPF values reaching 3.85 and 5.43,respectively,during the 11th-15th years of operation,maintaining high efficiency even after long-term operation.
文摘In just one and half minutes,more than fifty thousand died due to the 7.7 and 7.6 magnitude earthquakes that struck Turkey’s southeast on February 6,2023;thousands of families who barely escaped struggled to survive in the freezing weather.A warm shelter was the most basic requirement of these families.Container buildings are a rapid and easy solution to this issue.However,there is a need for a more effective and safe heating option than a wood fire for these buildings.In this study,cabin heaters,which allow truck drivers to warm up when they park their vehicles to sleep,are specially optimized for emergency shelters after an earthquake.An optimized fuzzy controller was developed to use in such buildings,which allows an air–fuel ratio in the combustion chamber of the cabin heater to be controlled adaptively based on system dynamics to get lower carbon emissions and fuel consumption.The TRNSYS software was used to establish the transient simulation model of a cabin heater with a capacity of 4 kW for a typical 21 m^(2) shelter building in Turkey’s cold regions.The developed fuzzy controller carried out the heating process of this shelter from the 15th of November to the 15th of March.Instead of using expert knowledge,the Gray Wolf Optimization(GWO)method was applied to optimize the fuzzy controller parameters developed for the cabin heater.With the optimized fuzzy controller,the fuel consumption at the end of the heating season was reduced by an average of 0.2 L/h,and the cabin heater’s efficiency increased by more than 13%.Our simulation results show that the intelligent controller we developed could improve diesel fuel combustion efficiency by up to 85%.
