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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.

This exciting research by Zachari Thiry, Massimiliano Ruocco, Alessandro Nocente, and Michail Spitieris dives into the innovative use of synthetic data to enhance indoor temperature forecasting for HVAC systems. By harnessing advanced AI techniques like GANs and VAEs, the study shows remarkable improvements in forecasting accuracy, even in data-scarce environments.

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