The Experts below are selected from a list of 40695 Experts worldwide ranked by ideXlab platform
Jeffrey Shaman - One of the best experts on this subject based on the ideXlab platform.
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Indoor Temperature and humidity in new york city apartments during winter
Science of The Total Environment, 2017Co-Authors: Ashlinn Quinn, Jeffrey ShamanAbstract:Abstract Background Concerns about Indoor residential humidity have largely centered on dampness prevention. Overly dry air, however, may favor the survival of some viruses and hence respiratory infections. Many residents employ portable humidifiers to humidify their home environment, yet the effect of these humidifiers on Indoor humidity is not known. Methods We monitored Indoor Temperature and humidity in 34 apartments in New York City during winter 2014–2015. We combined information from the monitors with surveyed information on building, household, and apartment-level factors and with information on household humidifier use. Using multilevel regression models, we investigated the role of these factors on Indoor absolute humidity levels during the winter. Results Mean Indoor vapor pressure (a measure of absolute humidity) was 6.7 mb in the surveyed homes during the winter season. Ownership of a humidifier was not associated with higher Indoor humidity levels; however, larger building size (above 100 units) was significantly associated with lower humidity. The presence of a radiator heating system was non-significantly associated with higher humidity. Conclusions The wintertime Indoor environment in this sample of New York City apartments is dry. Future research is needed to evaluate the effectiveness of portable humidifiers in the home and to clarify the relationship between dry Indoor air and the transmission of viral infections.
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socioeconomic and outdoor meteorological determinants of Indoor Temperature and humidity in new york city dwellings
Weather Climate and Society, 2013Co-Authors: James Tamerius, M S Perzanowski, Luis M Acosta, Judith S Jacobson, Inge F Goldstein, James W Quinn, Andrew Rundle, Jeffrey ShamanAbstract:AbstractNumerous mechanisms link outdoor weather and climate conditions to human health. It is likely that many health conditions are more directly affected by Indoor rather than outdoor conditions. Yet, the relationship between Indoor Temperature and humidity conditions to outdoor variability, and the heterogeneity of the relationship among different Indoor environments are largely unknown. The authors use 5–14-day measures of Indoor Temperature and relative humidity from 327 dwellings in New York City New York, for the years 2008–11 to investigate the relationship between Indoor climate, outdoor meteorological conditions, socioeconomic conditions, and building descriptors. Study households were primarily middle income and located across the boroughs of Brooklyn, Queens, Bronx, and Manhattan. Indoor Temperatures are positively associated with outdoor Temperature during the warm season and study dwellings in higher socioeconomic status neighborhoods are significantly cooler. During the cool season, outdoo...
Ashlinn Quinn - One of the best experts on this subject based on the ideXlab platform.
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Indoor Temperature and humidity in new york city apartments during winter
Science of The Total Environment, 2017Co-Authors: Ashlinn Quinn, Jeffrey ShamanAbstract:Abstract Background Concerns about Indoor residential humidity have largely centered on dampness prevention. Overly dry air, however, may favor the survival of some viruses and hence respiratory infections. Many residents employ portable humidifiers to humidify their home environment, yet the effect of these humidifiers on Indoor humidity is not known. Methods We monitored Indoor Temperature and humidity in 34 apartments in New York City during winter 2014–2015. We combined information from the monitors with surveyed information on building, household, and apartment-level factors and with information on household humidifier use. Using multilevel regression models, we investigated the role of these factors on Indoor absolute humidity levels during the winter. Results Mean Indoor vapor pressure (a measure of absolute humidity) was 6.7 mb in the surveyed homes during the winter season. Ownership of a humidifier was not associated with higher Indoor humidity levels; however, larger building size (above 100 units) was significantly associated with lower humidity. The presence of a radiator heating system was non-significantly associated with higher humidity. Conclusions The wintertime Indoor environment in this sample of New York City apartments is dry. Future research is needed to evaluate the effectiveness of portable humidifiers in the home and to clarify the relationship between dry Indoor air and the transmission of viral infections.
Tianyi Zhao - One of the best experts on this subject based on the ideXlab platform.
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Modeling for Indoor Temperature prediction based on time-delay and Elman neural network in air conditioning System
Journal of building engineering, 2020Co-Authors: Zongwei Han, Tianyi Zhao, Jili Zhang, Da XueAbstract:Abstract An effective Indoor Temperature model would assist in improving energy efficiency and Indoor thermal comfort of air conditioning system. However, it is difficult to build an accurate model due to lag response characteristic in the regulation process of Indoor Temperature. To solve this problem, the modeling and prediction methods for Indoor Temperature lag response characteristic based on time-delay neural network (TDNN) and Elman network neural (ENN) are presented. Then, taking variable air volume (VAV) air conditioning system as the study object, the effectiveness and practicability of proposed methods are validated using simulation sampling data and real-time operating data. Results indicate that ENN could be considered as a better modeling method for Indoor Temperature prediction for its simpler network structure, smaller storing space and better prediction accuracy. The contribution of this study is to provide an applicable online ANN modeling method for Indoor Temperature lag characteristic, and detailed training and validation for online implementation are presented, which will benefit for engineers and technicians to use in practical engineering. Meanwhile, this study provides the reference for online application of advanced intelligent algorithms in the building engineering.
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Model parameter identification of Indoor Temperature lag characteristic based on hysteresis relay feedback control in VAV systems
Journal of building engineering, 2019Co-Authors: Sida Lin, Jili Zhang, Tianyi ZhaoAbstract:Abstract The dynamic regulation process of variable air volume (VAV) systems is a typical nonlinear process, which has characteristics of multi-variable, strong coupling, and large lag. Due to the lag response characteristics of Indoor Temperature versus system regulation variables, it is difficult to accurately describe Indoor Temperature dynamic regulation characteristics based on a conventional open-loop step response test. Taking Indoor Temperature lag characteristic of the air conditioning system as the study object, this paper brings hysteresis relay feedback control (HRFC) into the model parameter identification of Indoor Temperature lag characteristic, and provides a novel method for Indoor Temperature lag characteristic identification of the air conditioning system. This study provides the detail implementation process of the proposed identification method. Supported by the comparative experimental study, the effectiveness and practicability of the proposed method is validated in an integrated control test rig for a VAV system. Under the condition of no prior knowledge, model parameters of Indoor Temperature first-order inertia plus lag link transfer function could be identified based on HRFC accurately. Results indicate that inertial time coefficients and pure lag time coefficients are convergent. The key contribution of this paper is to propose an online identification method for Indoor Temperature lag characteristic in dynamic regulation process of VAV system. It's worth noting that the proposed method may be invalid if the Indoor Temperature changes greatly due to some uncertainty large disturbances, however it is useful for new-built VAV system during the commissioning process.
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predication control for Indoor Temperature time delay using elman neural network in variable air volume system
Energy and Buildings, 2017Co-Authors: Xiuming Li, Tianyi Zhao, Jili Zhang, Tingting ChenAbstract:Abstract Aiming at the prediction control for Indoor Temperature time-delay in variable air volume (VAV) air conditioning system, this paper presents an Indoor Temperature prediction control method based on Elman neural network multi-step prediction model. Firstly, this paper introduces basic control principles of pressure-dependent and pressure-independent VAV terminal through comparable analysis and points out significance of Indoor Temperature prediction control based on pressure-dependent VAV terminal. Then, Elman neural network multi-step prediction model and corresponding Indoor Temperature prediction control method for pressure-dependent VAV terminal are proposed based on the fundamental principle of periodic prediction control for time-delay system. Finally, the effect of proposed prediction control method is validated by the experimental study according to the test data of supply air volume regulating process, provided that the supply air volume control loop adopts constant static pressure control method. Experimental results indicate the proposed Indoor Temperature prediction control method based on pressure-dependent VAV terminal could change the conventional regulating mode of the VAV air conditioning system, which will be benefit for improving the control stability of Indoor Temperature control loop and other corresponding control loops.
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The Influence of Doors and Windows on the Indoor Temperature in Rural House
Procedia Engineering, 2015Co-Authors: Nina Shao, Jili Zhang, Tianyi ZhaoAbstract:Abstract Owing to the cold and windy weather in southern Liaoning province in winter, the heat loss through doors and windows accounts for 20 to 30 percentage of the whole heat loss of house. To decline the heat loss through doors and windows, focusing on the typical rural house, an energy consumption system is built. On the basis of it, a study about the influence on the Indoor Temperature of windows’ heat transfer coefficient, airtightness, size and the outside door is made. The results show that on the condition of the same heat supply, the Temperature of the room, which installs single frame double glass windows, is 2 centigrade higher than which installs single frame single glass windows, and is 0.4 centigrade higher than which installs double frames single glass windows. The Indoor Temperature of room installing casement windows is 0.5 centigrade higher than the room installing sash windows. For the southern window, the heat flowing into the house is more than the heat losing through window, and the bigger the window, the higher the Indoor Temperature. However, regarding there is no direct solar radiation in the north, for the size of northern window, the little, the better. In rural house, the foyer can prevent cold air from flowing into the rooms directly. In other words, there is a Temperature buffer around rooms. The simulation results show that the existence of foyer can raise the Temperature of kitchen 1.1 centigrade. The study of this paper will offer theoretical basis to the new countryside construction.
Juan Pardo - One of the best experts on this subject based on the ideXlab platform.
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on line learning of Indoor Temperature forecasting models towards energy efficiency
Energy and Buildings, 2014Co-Authors: Francisco Zamoramartinez, Pablo Romeu, Paloma Botellarocamora, Juan PardoAbstract:Abstract The SMLsystem is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH) to participate in the Solar Decathlon 2012 competition. Several technologies have been integrated to reduce power consumption. A predictive module, based on artificial neural networks (ANNs), has been developed using data acquired in Valencia. The module produces short-term forecast of Indoor Temperature, using as input data captured by a complex monitoring system. The system expects to reduce the power consumption related to Heating, Ventilation and Air Conditioning (HVAC) system, due to the following assumptions: the high power consumption for which HVAC is responsible (53.9% of the overall consumption); and the energy needed to maintain Temperature is less than the energy required to lower/increase it. This paper studies the development viability of predictive systems for a totally unknown environment applying on-line learning techniques. The model parameters are estimated starting from a totally random model or from an unbiased a priori knowledge. These forecasting measures could allow the house to adapt itself to future Temperature conditions by using home automation in an energy-efficient manner. Experimental results show reasonable forecasting accuracy with simple models, and in relatively short training time (4–5 days).
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time series forecasting of Indoor Temperature using pre trained deep neural networks
International Conference on Artificial Neural Networks, 2013Co-Authors: Pablo Romeu, Francisco Zamoramartinez, Paloma Botellarocamora, Juan PardoAbstract:Artificial neural networks have proved to be good at time-series forecasting problems, being widely studied at literature. Traditionally, shallow architectures were used due to convergence problems when dealing with deep models. Recent research findings enable deep architectures training, opening a new interesting research area called deep learning. This paper presents a study of deep learning techniques applied to time-series forecasting in a real Indoor Temperature forecasting task, studying performance due to different hyper-parameter configurations. When using deep models, better generalization performance at test set and an over-fitting reduction has been observed.
Jili Zhang - One of the best experts on this subject based on the ideXlab platform.
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Modeling for Indoor Temperature prediction based on time-delay and Elman neural network in air conditioning System
Journal of building engineering, 2020Co-Authors: Zongwei Han, Tianyi Zhao, Jili Zhang, Da XueAbstract:Abstract An effective Indoor Temperature model would assist in improving energy efficiency and Indoor thermal comfort of air conditioning system. However, it is difficult to build an accurate model due to lag response characteristic in the regulation process of Indoor Temperature. To solve this problem, the modeling and prediction methods for Indoor Temperature lag response characteristic based on time-delay neural network (TDNN) and Elman network neural (ENN) are presented. Then, taking variable air volume (VAV) air conditioning system as the study object, the effectiveness and practicability of proposed methods are validated using simulation sampling data and real-time operating data. Results indicate that ENN could be considered as a better modeling method for Indoor Temperature prediction for its simpler network structure, smaller storing space and better prediction accuracy. The contribution of this study is to provide an applicable online ANN modeling method for Indoor Temperature lag characteristic, and detailed training and validation for online implementation are presented, which will benefit for engineers and technicians to use in practical engineering. Meanwhile, this study provides the reference for online application of advanced intelligent algorithms in the building engineering.
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Model parameter identification of Indoor Temperature lag characteristic based on hysteresis relay feedback control in VAV systems
Journal of building engineering, 2019Co-Authors: Sida Lin, Jili Zhang, Tianyi ZhaoAbstract:Abstract The dynamic regulation process of variable air volume (VAV) systems is a typical nonlinear process, which has characteristics of multi-variable, strong coupling, and large lag. Due to the lag response characteristics of Indoor Temperature versus system regulation variables, it is difficult to accurately describe Indoor Temperature dynamic regulation characteristics based on a conventional open-loop step response test. Taking Indoor Temperature lag characteristic of the air conditioning system as the study object, this paper brings hysteresis relay feedback control (HRFC) into the model parameter identification of Indoor Temperature lag characteristic, and provides a novel method for Indoor Temperature lag characteristic identification of the air conditioning system. This study provides the detail implementation process of the proposed identification method. Supported by the comparative experimental study, the effectiveness and practicability of the proposed method is validated in an integrated control test rig for a VAV system. Under the condition of no prior knowledge, model parameters of Indoor Temperature first-order inertia plus lag link transfer function could be identified based on HRFC accurately. Results indicate that inertial time coefficients and pure lag time coefficients are convergent. The key contribution of this paper is to propose an online identification method for Indoor Temperature lag characteristic in dynamic regulation process of VAV system. It's worth noting that the proposed method may be invalid if the Indoor Temperature changes greatly due to some uncertainty large disturbances, however it is useful for new-built VAV system during the commissioning process.
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predication control for Indoor Temperature time delay using elman neural network in variable air volume system
Energy and Buildings, 2017Co-Authors: Xiuming Li, Tianyi Zhao, Jili Zhang, Tingting ChenAbstract:Abstract Aiming at the prediction control for Indoor Temperature time-delay in variable air volume (VAV) air conditioning system, this paper presents an Indoor Temperature prediction control method based on Elman neural network multi-step prediction model. Firstly, this paper introduces basic control principles of pressure-dependent and pressure-independent VAV terminal through comparable analysis and points out significance of Indoor Temperature prediction control based on pressure-dependent VAV terminal. Then, Elman neural network multi-step prediction model and corresponding Indoor Temperature prediction control method for pressure-dependent VAV terminal are proposed based on the fundamental principle of periodic prediction control for time-delay system. Finally, the effect of proposed prediction control method is validated by the experimental study according to the test data of supply air volume regulating process, provided that the supply air volume control loop adopts constant static pressure control method. Experimental results indicate the proposed Indoor Temperature prediction control method based on pressure-dependent VAV terminal could change the conventional regulating mode of the VAV air conditioning system, which will be benefit for improving the control stability of Indoor Temperature control loop and other corresponding control loops.
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The Influence of Doors and Windows on the Indoor Temperature in Rural House
Procedia Engineering, 2015Co-Authors: Nina Shao, Jili Zhang, Tianyi ZhaoAbstract:Abstract Owing to the cold and windy weather in southern Liaoning province in winter, the heat loss through doors and windows accounts for 20 to 30 percentage of the whole heat loss of house. To decline the heat loss through doors and windows, focusing on the typical rural house, an energy consumption system is built. On the basis of it, a study about the influence on the Indoor Temperature of windows’ heat transfer coefficient, airtightness, size and the outside door is made. The results show that on the condition of the same heat supply, the Temperature of the room, which installs single frame double glass windows, is 2 centigrade higher than which installs single frame single glass windows, and is 0.4 centigrade higher than which installs double frames single glass windows. The Indoor Temperature of room installing casement windows is 0.5 centigrade higher than the room installing sash windows. For the southern window, the heat flowing into the house is more than the heat losing through window, and the bigger the window, the higher the Indoor Temperature. However, regarding there is no direct solar radiation in the north, for the size of northern window, the little, the better. In rural house, the foyer can prevent cold air from flowing into the rooms directly. In other words, there is a Temperature buffer around rooms. The simulation results show that the existence of foyer can raise the Temperature of kitchen 1.1 centigrade. The study of this paper will offer theoretical basis to the new countryside construction.