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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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Thermal comfort is a critical component of indoor environments, especially in schools where learning is the main objective. However, thermal comfort comes at a price that many schools are unable to afford. Therefore, it is critical to determine a method to lower the energy costs of a building while still maintaining occupant thermal comfort. The objective of this study is to investigate how three indoor environmental parameters of air speed, humidity, and air temperature influence energy and...
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Increased thermal comfort in buildings is consuming large amounts of energy around the world, especially in hot arid and semi-arid regions. Finding and adapting ways to naturally cool buildings should be a priority for researchers in the subject. For centuries the Middle East cultures have used wind towers to cool their buildings and they have proved to be a cost-effective, easy to implement and reliable solution for passive cooling that requires almost negligible energy to operate. The...
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A simplified modelling framework for the prediction of the indoor environment, energy use and socioeconomic consequences of improving air quality and temperature in school buildings is suggested. The framework combines established models for infiltration and different modes of ventilation to estimate yearly distributions of the classroom temperature and CO2 concentration. These distributions are used as input to a prediction of pupil performance of schoolwork, their attendance at school, and...
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Theme
- Sustainable Development Goals
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Artificial Intelligence
(3)
- Machine learning (1)
- Neural network (2)
- Disability (1)
- Education and climate change (2)
- Energy efficiency (1)
- Indoor Environmental Quality (IEQ) (2)
- Modelling (1)
- Noise (1)
- Retrofits (1)
- School buildings and classrooms (1)
- Thermal comfort and heat stress (4)