This paper proposes an energy and carbon footprint modelling using artificial intelligence technique to assess the impact of occupant density for various types of office building.We use EnergyPlus to simulate energy c...This paper proposes an energy and carbon footprint modelling using artificial intelligence technique to assess the impact of occupant density for various types of office building.We use EnergyPlus to simulate energy con-sumption,and then estimate the related CO₂emissions based on three years(2016–2018)of Actual Meteoro-logical Year(AMY)weather data.Various occupant densities were used to evaluate the annual energy consumption and CO₂emission.In this work,a robust deep learning technique of long short-term memory(LSTM)model was established to predict the time-series energy consumption and CO₂emissions.A power exponential curve was suggested to correlate the behaviour of annual energy and CO₂emission for occupant densities range from 10 to 100 m2/person for each office building type.The results of LSTM model show high prediction performance and small variations within the three types of office building data,which can be applied to the similar building model to predict and optimise energy consumption and CO₂emission.展开更多
Using the approach of D. Landriault et al. and B. Li and X. Zhou, for a one-dimensional time-homogeneous diffusion process X and constants c 〈 a 〈 b 〈 d, we find expressions of double Laplace transforms of the form...Using the approach of D. Landriault et al. and B. Li and X. Zhou, for a one-dimensional time-homogeneous diffusion process X and constants c 〈 a 〈 b 〈 d, we find expressions of double Laplace transforms of the form Ex[e--θTd--λ∫o Td1a 〈Xs〈b ds; Td 〈 Tc], where Tx denotes the first passage time of level x. As applications, we find explicit Laplace transforms of the corresponding occupation time and occupation density for the Brownian motion with two-valued drift and that of occupation time for the skew Ornstein- Uhlenbeck process, respectively. Some known results are also recovered.展开更多
基金This work has been supported by the UK’s innovation agency,Innovate UK,through the project with Ref.104317,titled“Occupancy enhanced smart city maps”.
文摘This paper proposes an energy and carbon footprint modelling using artificial intelligence technique to assess the impact of occupant density for various types of office building.We use EnergyPlus to simulate energy con-sumption,and then estimate the related CO₂emissions based on three years(2016–2018)of Actual Meteoro-logical Year(AMY)weather data.Various occupant densities were used to evaluate the annual energy consumption and CO₂emission.In this work,a robust deep learning technique of long short-term memory(LSTM)model was established to predict the time-series energy consumption and CO₂emissions.A power exponential curve was suggested to correlate the behaviour of annual energy and CO₂emission for occupant densities range from 10 to 100 m2/person for each office building type.The results of LSTM model show high prediction performance and small variations within the three types of office building data,which can be applied to the similar building model to predict and optimise energy consumption and CO₂emission.
基金Acknowledgements The authors thank the anonymous referees for helpful comments. Yingqiu Li＇s work was supported by the National Natural Science Foundation of China （Grant No. 11171044） und the Natural Science Foundation of Hunan Province （Grant No. llJ32001） Suxin Wang＇s work was supported by the Natural Sciences and Engineering Research Council of Canada.
文摘Using the approach of D. Landriault et al. and B. Li and X. Zhou, for a one-dimensional time-homogeneous diffusion process X and constants c 〈 a 〈 b 〈 d, we find expressions of double Laplace transforms of the form Ex[e--θTd--λ∫o Td1a 〈Xs〈b ds; Td 〈 Tc], where Tx denotes the first passage time of level x. As applications, we find explicit Laplace transforms of the corresponding occupation time and occupation density for the Brownian motion with two-valued drift and that of occupation time for the skew Ornstein- Uhlenbeck process, respectively. Some known results are also recovered.
