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Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments

Engineering and Technology

Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments

Z. Thiry, M. Ruocco, et al.

In a groundbreaking study by Zachari Thiry, Massimiliano Ruocco, Alessandro Nocente, and Michail Spitieris, the potential of synthetic data generated through AI methods like GANs and VAEs to enhance indoor temperature forecasting is explored. This research demonstrates how augmenting real data can significantly boost forecasting accuracy and reduce training variance, addressing a critical challenge in HVAC system control.

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