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Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air...
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Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep...
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The use of Artificial Intelligence (AI) technologies in buildings can assist in reducing energy consumption through enhanced control, automation, and reliability. This review aims to explore the use of AI to enhance energy efficiency throughout various stages of the building lifecycle, including building design, construction, operation and control, maintenance, and retrofit. The review encompasses multiple studies in the field published between 2018 and 2023. These studies were identified...
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This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the...
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High ambient air temperatures in Africa pose significant health and behavioral challenges in populations with limited access to cooling adaptations. The built environment can exacerbate heat exposure, making passive home cooling adaptations a potential method for protecting occupants against indoor heat exposure.
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This study explores the imperative need for decolonizing climate change adaptation strategies by focusing on Indigenous knowledge and perspectives. Focusing on the Munda Indigenous communities residing in the coastal areas of Bangladesh, the research offers critical insights into the intricate relationship between Indigenous wisdom and sustainable climate adaptation. By engaging with the Munda Indigenous people and their traditions, this study explores how traditional ecological knowledge...
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This study explores the imperative need for decolonizing climate change adaptation strategies by focusing on Indigenous knowledge and perspectives. Focusing on the Munda Indigenous communities residing in the coastal areas of Bangladesh, the research offers critical insights into the intricate relationship between Indigenous wisdom and sustainable climate adaptation. By engaging with the Munda Indigenous people and their traditions, this study explores how traditional ecological knowledge...
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As smart thermostats become increasingly available in residential buildings, there is an opportunity to use measured building data to calibrate models for community and district applications, instead of relying on high-fidelity simulations. This study used smart thermostat data from 60,000 houses in North America to create single-zone models. The model structure was defined with an automated forward selection procedure. 61% of the final models were classified as good fits and the structure...
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To meet the thermal comfort requirements of room occupants, a fast and accurate method for predicting indoor high-resolution 3D airflow distribution is necessary, which can be combined with heating, ventilation, and air conditioning (HVAC) systems to adjust the indoor environment. Artificial neural networks (ANN) can establish complex mappings between variables with nonlinear relationships. The aim of this study was to verify the feasibility of an ANN for the fast and accurate prediction of...