Adaptive Optimal Control of a Grid-Independent Photovoltaic System

Solar Energy ◽  
2002 ◽  
Author(s):  
Gregor P. Henze ◽  
Robert H. Dodier
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Learning Algorithm ◽  
Photovoltaic System ◽  
Control Performance ◽  
Adaptive Optimal Control ◽  
Q Learning ◽  
Model Free ◽  
Base Load ◽  
Better Than

This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.

Adaptive Optimal Control of a Grid-Independent Photovoltaic System

2003 ◽  
Vol 125 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Gregor P. Henze ◽  
Robert H. Dodier
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Learning Algorithm ◽  
Photovoltaic System ◽  
Control Performance ◽  
Adaptive Optimal Control ◽  
Q Learning ◽  
Model Free ◽  
Base Load ◽  
Better Than

This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The control algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.

scholarly journals Model Free Optimal Control of Two Whole Buildings using Deep Q-Learning

2020 ◽  
Author(s):  
Ki Uhn Ahn ◽  
Jae Min Kim ◽  
Youngsub Kim ◽  
Cheol Soo Park ◽  
Kwang Woo Kim
Keyword(s):  
Optimal Control ◽  
Q Learning ◽  
Model Free

scholarly journals Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path

2020 ◽  
Vol 8 (6) ◽  
pp. 4333-4338
Keyword(s):  
Reinforcement Learning ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Optimal Path ◽  
Autonomous Mobile Robots ◽  
Current Standard ◽  
Q Learning ◽  
Better Than

This paper presents a thorough comparative analysis of various reinforcement learning algorithms used by autonomous mobile robots for optimal path finding and, we propose a new algorithm called Iterative SARSA for the same. The main objective of the paper is to differentiate between the Q-learning and SARSA, and modify the latter. These algorithms use either the on-policy or off-policy methods of reinforcement learning. For the on-policy method, we have used the SARSA algorithm and for the off-policy method, the Q-learning algorithm has been used. These algorithms also have an impacting effect on finding the shortest path possible for the robot. Based on the results obtained, we have concluded how our algorithm is better than the current standard reinforcement learning algorithms

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