  Forecasting indoor temperatures is important to achieve efficient control of
HVAC systems. In this task, the limited data availability presents a challenge
as most of the available data is acquired during standard operation where
extreme scenarios and transitory regimes such as major temperature increases or
decreases are de-facto excluded. Acquisition of such data requires significant
energy consumption and a dedicated facility, hindering the quantity and
diversity of available data. Cost related constraints however do not allow for
continuous year-around acquisition. To address this, we investigate the
efficacy of data augmentation techniques leveraging SoTA AI-based methods for
synthetic data generation. Inspired by practical and experimental motivations,
we explore fusion strategies of real and synthetic data to improve forecasting
models. This approach alleviates the need for continuously acquiring extensive
time series data, especially in contexts involving repetitive heating and
cooling cycles in buildings. In our evaluation 1) we assess the performance of
synthetic data generators independently, particularly focusing on SoTA AI-based
methods; 2) we measure the utility of incorporating synthetically augmented
data in a subsequent forecasting tasks where we employ a simple model in two
distinct scenarios: 1) we first examine an augmentation technique that combines
real and synthetically generated data to expand the training dataset, 2) we
delve into utilizing synthetic data to tackle dataset imbalances. Our results
highlight the potential of synthetic data augmentation in enhancing forecasting
accuracy while mitigating training variance. Through empirical experiments, we
show significant improvements achievable by integrating synthetic data, thereby
paving the way for more robust forecasting models in low-data regime.
