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assessment of human temperature. Indoor thermal comfort is perceived in humans with wide variance, by a variety of methods and does not have one industry standardised validated method. Equally, the current
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shaving/shifting, voltage and frequency support, and virtual inertial response. Due to the volatile and intermittent nature of RESs, in this project, machine learning (ML) methods are used to accurately
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assessment of human temperature. Indoor thermal comfort is perceived in humans with wide variance, by a variety of methods and does not have one industry standardised validated method. Equally, the current
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approaches raise computational cost, energy consumption and turnaround time, placing increasing pressure on sustainability targets. Understanding how geometric changes influence the flow, thermal or structural
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will