  Utilizing solar energy to meet space heating and domestic hot water demand is
very efficient (in terms of environmental footprint as well as cost), but in
order to ensure that user demand is entirely covered throughout the year needs
to be complemented with auxiliary heating systems, typically boilers and heat
pumps. Naturally, the optimal control of such a system depends on an accurate
prediction of solar thermal production.
  Experimental testing and physics-based numerical models are used to find a
collector's performance curve - the mapping from solar radiation and other
external conditions to heat production - but this curve changes over time once
the collector is exposed to outdoor conditions. In order to deploy advanced
control strategies in small domestic installations, we present an approach that
uses machine learning to automatically construct and continuously adapt a model
that predicts heat production. Our design is driven by the need to (a)
construct and adapt models using supervision that can be extracted from
low-cost instrumentation, avoiding extreme accuracy and reliability
requirements; and (b) at inference time, use inputs that are typically provided
in publicly available weather forecasts.
  Recent developments in attention-based machine learning, as well as careful
adaptation of the training setup to the specifics of the task, have allowed us
to design a machine learning-based solution that covers our requirements. We
present positive empirical results for the predictive accuracy of our solution,
and discuss the impact of these results on the end-to-end system.
