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South Africa’s private sector – under significant pressure to become energy efficient and employ sustainability principles – has long been implementing energy-saving mechanisms. Unfortunately, there seems to exist many misplaced incentives in South Africa’s public sector that prevent it from embracing energy-efficient technology. With the falling cost of LED lighting and the rising cost of electricity, however, conversions are increasingly cost-efficient. Effecting these changes are...
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This paper presents a methodology for the development and implementation of Model Predictive Control (MPC) in institutional buildings. This methodology relies on Artificial Intelligence (AI) for model development. An appropriate control-oriented model is a critical component in MPC; model development is no easy task, and it often requires significant technical expertise, effort and time, along with a substantial amount of information. AI techniques enable rapid development and calibration of...
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Climate change is directly and disproportionately threatening the right to health of people with disabilities due to higher ambient temperatures, elevated air pollutants, and increasing exposure to extreme weather events that include heatwaves, floods, hurricanes, and wildfires. Strikingly, the global mortality rate of people with disabilities in natural disasters is up to four times higher than people without disabilities due to a scarcity of inclusive planning, accessible information,...
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School building stock retrofit forms a key part of UK’s commitment to net-zero carbon target by 2050. However, with a changing climate, the retrofit of school buildings may have unintended consequences on classroom thermal environments and cognitive performance of children in non-heating seasons. This paper aims to quantify the impact of school stock retrofit in accordance with increasingly tightening energy efficiency regulatory requirements on cognitive performance of English children,...
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Machine learning presents opportunities for tracking evidence on climate change adaptation, including text-based methods from natural language processing. In theory, such tools can analyse more data in less time, using fewer resources and with less risk of bias. However, the first generation of adaptation studies have delivered only proof of concepts. Reviewing these first studies, we argue that future efforts should focus on creating more diverse datasets, investigating concrete hypotheses,...
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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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Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...
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Artificial Intelligence
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- Machine learning (8)
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- Thermal comfort and heat stress (6)