  The prediction of solar power generation is a challenging task due to its
dependence on climatic characteristics that exhibit spatial and temporal
variability. The performance of a prediction model may vary across different
places due to changes in data distribution, resulting in a model that works
well in one region but not in others. Furthermore, as a consequence of global
warming, there is a notable acceleration in the alteration of weather patterns
on an annual basis. This phenomenon introduces the potential for diminished
efficacy of existing models, even within the same geographical region, as time
progresses. In this paper, a domain adaptive deep learning-based framework is
proposed to estimate solar power generation using weather features that can
solve the aforementioned challenges. A feed-forward deep convolutional network
model is trained for a known location dataset in a supervised manner and
utilized to predict the solar power of an unknown location later. This adaptive
data-driven approach exhibits notable advantages in terms of computing speed,
storage efficiency, and its ability to improve outcomes in scenarios where
state-of-the-art non-adaptive methods fail. Our method has shown an improvement
of $10.47 \%$, $7.44 \%$, $5.11\%$ in solar power prediction accuracy compared
to best performing non-adaptive method for California (CA), Florida (FL) and
New York (NY), respectively.
