@article{Panagopoulos_Chalkiadakis_Jennings_2015, title={Towards Optimal Solar Tracking: A Dynamic Programming Approach}, volume={29}, url={https://ojs.aaai.org/index.php/AAAI/article/view/9244}, DOI={10.1609/aaai.v29i1.9244}, abstractNote={ <p> The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on-line providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Panagopoulos, Athanasios Aris and Chalkiadakis, Georgios and Jennings, Nicholas}, year={2015}, month={Feb.} }