Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Ruelens, Frederik"'
Publikováno v:
CSEE Journal of Power and Energy Systems, Vol. 5, Iss. 4, Dec. 2019, pp. 423-432
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment. Extracting a relevant set of features from these observations is a challenging task and may require subs
Externí odkaz:
http://arxiv.org/abs/1707.08553
Optimal control of thermostatically controlled loads connected to a district heating network is considered a sequential decision- making problem under uncertainty. The practicality of a direct model-based approach is compromised by two challenges, na
Externí odkaz:
http://arxiv.org/abs/1701.08074
Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This work proposes a novel approach that uses a convolutional neural network to extract hidden
Externí odkaz:
http://arxiv.org/abs/1604.08382
Autor:
Ruelens, Frederik, Claessens, Bert, Quaiyum, Salman, De Schutter, Bart, Babuska, Robert, Belmans, Ronnie
Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of
Externí odkaz:
http://arxiv.org/abs/1512.00408
Driven by the opportunity to harvest the flexibility related to building climate control for demand response applications, this work presents a data-driven control approach building upon recent advancements in reinforcement learning. More specificall
Externí odkaz:
http://arxiv.org/abs/1507.03638
The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more effici
Externí odkaz:
http://arxiv.org/abs/1506.01054
Autor:
Ruelens, Frederik, Claessens, Bert, Vandael, Stijn, De Schutter, Bart, Babuska, Robert, Belmans, Ronnie
Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification s
Externí odkaz:
http://arxiv.org/abs/1504.02125
Autor:
Mbuwir, Brida V.1,2 brida.mbuwir@vito.be, Ruelens, Frederik1,2 frederik.ruelens@esat.kuleuven.be, Spiessens, Fred2,3 fred.spiessens@vito.be, Deconinck, Geert1,2 geert.deconinck@esat.kuleuven.be
Publikováno v:
Energies (19961073). Nov2017, Vol. 10 Issue 11, p1846. 19p.
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