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Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease...
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Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. Existing studies have addressed the monitoring of thermal comfort through questionnaires and activities involving occupants. However, few studies have considered disabled people in the monitoring of thermal comfort, despite the potential for impairments to present thermal requirements that are significantly different from those of an occupant...
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The development of deep learning (DL) technology provides an opportunity for accurate prediction and effective control of complex building systems. However, despite the high prediction performance of DL models, few studies integrate DL with the model predictive control (MPC) algorithm for improving building management. In this study, a DL-based MPC framework using encoder-decoder recurrent neural network is developed for real-time control of building thermal environment to exploit the...
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The development of deep learning (DL) technology provides an opportunity for accurate prediction and effective control of complex building systems. However, despite the high prediction performance of DL models, few studies integrate DL with the model predictive control (MPC) algorithm for improving building management. In this study, a DL-based MPC framework using encoder-decoder recurrent neural network is developed for real-time control of building thermal environment to exploit the...
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Several relationships between air temperature and work performance have been published. We reanalysed the one developed in 2006 by Seppänen et al.; which is probably the best known. We found that even when significant, its prediction accuracy is very low (R2 = 0.05, MAE = 1.9%, RMSE = 3.1%). We consequently reviewed the literature and found 35 studies on the effects of temperature on office work performance. We used Seppänen et al.’s approach to normalise the data reported in these studies...
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Weather data is a crucial input for myriad applications in the built environment, including building energy modeling and daylight analysis. Building science practitioners and researchers have been able to select from a variety of weather files, such as Weather Year for Energy Calculation 2 (WYEC2) and the Typical Meteorological Year (TMY). However, commonly used weather files are typically synthesized to represent trends over a relatively longer periods of time, and are often unable to...
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Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that...
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Housing markets are known to be affected by adverse environments (i.e., environmental air pollution incidents affect Indian urban residents). Urban atmosphere quality has changed extensively with PM2.5 and O3 becoming the primary atmosphere indicators of concern because of dense cities in recent years. There is a correlation between the air pollution of Amaravati with the housing market model. When estimating the housing market, the chapter makes use of the extended regression model together...
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<b>Background</b> The global literature on the links between climate change and human health is large, increasing exponentially, and it is no longer feasible to collate and synthesise using traditional systematic evidence mapping approaches. We aimed to use machine learning methods to systematically synthesise an evidence base on climate change and human health. <br><b>Methods</b> We used supervised machine learning and other natural language processing methods (topic modelling and...