Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI: | http://purl.tuc.gr/dl/dias/6E973991-514F-434E-82A8-DFCE1C1B9B6E | ||
| Έτος | 2015 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | A. A. Panagopoulos , G. Chalkiadakis, R. N. Jennings .(2015). Towards optimal solar tracking: a dynamic programming approach. Presened at 29th AAAI Conference on Artificial Intelligence , Austin, US, 25 - 30 Jan 2015. [online].Available :http://eprints.soton.ac.uk/372197/1/pcj2014.pdf | ||
| Εμφανίζεται στις Συλλογές |
The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking tech- niques. 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 con- trollers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into ac- count. 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 consump- tion model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.
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