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Towards optimal solar tracking: a dynamic programming approach

Towards optimal solar tracking: a dynamic programming approach
Towards optimal solar tracking: a dynamic programming approach
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 online 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.
695-701
Panagopoulos, Athanasios Aris
437c0cf3-6abb-481a-9fd4-4da1efb68867
Chalkiadakis, Georgios
50ef5d10-3ffe-4253-ac88-fad4004240e7
Jennings, R. Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Panagopoulos, Athanasios Aris
437c0cf3-6abb-481a-9fd4-4da1efb68867
Chalkiadakis, Georgios
50ef5d10-3ffe-4253-ac88-fad4004240e7
Jennings, R. Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Panagopoulos, Athanasios Aris, Chalkiadakis, Georgios and Jennings, R. Nicholas (2015) Towards optimal solar tracking: a dynamic programming approach. AAAI-2015: 29th AAAI Conference on Artificial Intelligence, United States. 25 - 30 Jan 2015. pp. 695-701 .

Record type: Conference or Workshop Item (Paper)

Abstract

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 online 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.

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e-pub ahead of print date: January 2015
Published date: 2015
Venue - Dates: AAAI-2015: 29th AAAI Conference on Artificial Intelligence, United States, 2015-01-25 - 2015-01-30
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 372197
URI: http://eprints.soton.ac.uk/id/eprint/372197
PURE UUID: a464989f-8d63-4226-ab1b-64e8925ec94d

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Date deposited: 03 Dec 2014 15:39
Last modified: 28 Oct 2019 20:51

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