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.

利用太阳能满足空气供暖和生活热水需求在环境和成本方面非常高效，但要确保全年用户需求得到满足，需要结合辅助供暖系统，通常为锅炉和热泵。为了在小型家庭装置中部署先进的控制策略，我们提出了一种利用机器学习自动构建和持续适应预测热量生产模型的方法，该设计基于从低成本仪器中提取的监督信息构建和调整模型，避免了极高的准确性和可靠性要求；在推理时，使用通常在公开的天气预报中提供的输入。通过注意力机制的机器学习解决方案满足了我们的要求，我们提出了解决方案的良好实证结果，并讨论了这些结果对整个系统的影响。