Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Battery Life Estimation of Mobile Embedded Systems
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Causal architecture, complexity and self-organization in time series and cellular automata
Causal architecture, complexity and self-organization in time series and cellular automata
Finding approximate pomdp solutions through belief compression
Finding approximate pomdp solutions through belief compression
Battery Model for Embedded Systems
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
Pattern Learning and Decision Making in a Photovoltaic System
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Solving POMDPs by searching the space of finite policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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We propose a multiscale algorithm for autonomous agents to adaptively manage the operation of storage-enabled photovoltaic (PV) facilities based upon sequential decisions in a partially observable environment. The stochastic environment is learned and modeled by an approach called an ε-Machine, which operates on a set of a priori determined temporal scales, to give the agent an additional degree of freedom when optimizing its control decisions. We compare the performance of the proposed scheme with those of (1) control decisions based on a heuristic environment model rather than systematic learning, and (2) decisions made on a pre-determined scale. We argue that the systematic environment learning on multiple temporal scales makes the agent highly adaptable and, as a result, able to demonstrate a superior capability in managing the PV-storage facility according to a predetermined objective. The particular application focused upon in the paper is in managing distributed PV facilities as potential replacement of conventional peaking power plants.