Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applica...Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the cause of the current coronavirus disease 2019(COVID-19)pandemic,is evolving.Thus,the risk of airborne transmission in confined spaces may be higher,and c...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the cause of the current coronavirus disease 2019(COVID-19)pandemic,is evolving.Thus,the risk of airborne transmission in confined spaces may be higher,and corresponding precautions should be re-appraised.Here,we obtained the quantum generation rate(q)value of three SARS-CoV-2 variants(Alpha,Delta,and Omicron)for the Wells-Riley equation with a reproductive number-based fitted approach and estimated the association between the infection probability and ventilation rates.The q value was 89–165 h^(−1)for Alpha variant,312–935 h^(−1)for Delta variant,and 725–2,345 h^(−1)for Omicron variant.The ventilation rates increased to ensure an infection probability of less than 1%,and were 8,000–14,000 m^(3)h^(−1),26,000–80,000 m^(3)h^(−1),and 64,000–250,000 m^(3)h^(−1)per infector for the Alpha,Delta,and Omicron variants,respectively.If the infector and susceptible person wore N95 masks,the required ventilation rates decreased to about 1/100 of the values required without masks,which can be achieved in most typical scenarios.An air purifier was ineffective for reducing transmission when used in scenarios without masks.Preventing prolonged exposure time in confined spaces remains critical in reducing the risk of airborne transmission for highly contagious SARS-CoV-2 variants.展开更多
文摘Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.
基金We are grateful to Miss Ye Wang and the support provided by the Tsinghua University Spring Breeze Fund(2020Z99CFZ025).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the cause of the current coronavirus disease 2019(COVID-19)pandemic,is evolving.Thus,the risk of airborne transmission in confined spaces may be higher,and corresponding precautions should be re-appraised.Here,we obtained the quantum generation rate(q)value of three SARS-CoV-2 variants(Alpha,Delta,and Omicron)for the Wells-Riley equation with a reproductive number-based fitted approach and estimated the association between the infection probability and ventilation rates.The q value was 89–165 h^(−1)for Alpha variant,312–935 h^(−1)for Delta variant,and 725–2,345 h^(−1)for Omicron variant.The ventilation rates increased to ensure an infection probability of less than 1%,and were 8,000–14,000 m^(3)h^(−1),26,000–80,000 m^(3)h^(−1),and 64,000–250,000 m^(3)h^(−1)per infector for the Alpha,Delta,and Omicron variants,respectively.If the infector and susceptible person wore N95 masks,the required ventilation rates decreased to about 1/100 of the values required without masks,which can be achieved in most typical scenarios.An air purifier was ineffective for reducing transmission when used in scenarios without masks.Preventing prolonged exposure time in confined spaces remains critical in reducing the risk of airborne transmission for highly contagious SARS-CoV-2 variants.
