dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Vreeswijk, Gerard | |
dc.contributor.advisor | Rin, Benjamin | |
dc.contributor.author | Hoeij Schilthouwer Pompe, L. van | |
dc.date.accessioned | 2017-08-30T18:00:56Z | |
dc.date.available | 2017-08-30T18:00:56Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/27079 | |
dc.description.abstract | In recent work concerning demand response for flexibility carriers, specialized
methods have achieved considerable progress for specialized instances of carriers.
These methods often rely on complicated design decisions in feature engineer-
ing or utility function design. Furthermore, flexibility carriers are often stochastic
in their behaviour. In this paper we propose a general model-free reinforcement
learning approach using limited feature engineering and a straightforward utility
function. We validate our approach on a simulation of a flexibility carrying cold
storage cell. Our results indicate significant cost savings can be achieved through
our approach, at the cost of a long exploration period. Our approach requires ap-
proximately 69 simulated days before offering an improvement in cost over stan-
dard carrier behaviour. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 361622 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Demand Response for Flexibility Carriers
using Reinforcement Learning | |
dc.type.content | Bachelor Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | demand response; reinforcement learning; gradient boosting; function approximation | |
dc.subject.courseuu | Kunstmatige Intelligentie | |