2024/05/16 by Tatiana Boura, Boura, Tatiana, Natalia Koliou +7
Computer Science · Energy · Engineering · #Artificial Intelligence (cs.AI) #Economics #Electrical engineering #Energy Load and Power Forecasting #Engineering #Environmental economics #Environmental science #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Microeconomics #Production (economics) #Renewable energy #Solar Radiation and Photovoltaics #Solar Thermal and Photovoltaic Systems #Solar energy #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.09972
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.