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 Improved Q-Learning Method for Linear Discrete-Time Systems

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 368
Author(s):  
Jian Chen ◽  
Jinhua Wang ◽  
Jie Huang
Keyword(s):  
Optimal Control ◽  
Linear Systems ◽  
Discrete Time ◽  
Control Method ◽  
Learning Method ◽  
Control Laws ◽  
Q Learning ◽  
Model Free ◽  
Quadratic Optimal Control ◽  
Time Linear

In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have been made: (1) The correlation of data is the key factor in the success for the calculation of quadratic optimal control laws by Q-learning method; (2) The control laws for linear systems cannot be derived directly by the existing Q-learning method; (3) For nonlinear systems, there are some doubts about the data independence of current method. Therefore, it is necessary to discuss the probability of the controllers established by the existing Q-learning method. To solve this problem, based on the ridge regression, an improved model-free Q-learning quadratic optimal control method for discrete-time linear systems is proposed in this paper. Therefore, the computation process can be implemented correctly, and the effective controller can be solved. The simulation results show that the proposed method can not only overcome the problem caused by the data correlation, but also derive proper control laws for discrete-time linear systems.

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.

scholarly journals Quantized measurements in Q-learning based model-free optimal control

2020 ◽  
Vol 53 (2) ◽  
pp. 1640-1645
Author(s):  
Sini Tiistola ◽  
Risto Ritala ◽  
Matti Vilkko
Keyword(s):  
Optimal Control ◽  
Q Learning ◽  
Model Free

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 Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

2021 ◽  
Vol 4 (2) ◽  
pp. 55-76
Author(s):  
Dan Oyuga Anne ◽  
Elizaphan Maina
Keyword(s):  
Learning Style ◽  
Affective State ◽  
Learner Characteristics ◽  
Q Learning ◽  
Model Free ◽  
Learning Platform ◽  
Learning Path ◽  
Multiple Characteristics ◽  
E Learning ◽  
Extraction Model

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

Model-Free Optimal Control of VAR Resources in Distribution Systems: An Extremum Seeking Approach

2016 ◽  
Vol 31 (5) ◽  
pp. 3583-3593 ◽  
Author(s):  
Daniel B. Arnold ◽  
Matias Negrete-Pincetic ◽  
Michael D. Sankur ◽  
David M. Auslander ◽  
Duncan S. Callaway
Keyword(s):  
Optimal Control ◽  
Distribution Systems ◽  
Extremum Seeking ◽  
Model Free

Energy Optimization of Solar Micro-Grid Using Multi Agent Reinforcement Learning

2015 ◽  
Vol 787 ◽  
pp. 843-847
Author(s):  
Leo Raju ◽  
R.S. Milton ◽  
S. Sakthiyanandan
Keyword(s):  
Reinforcement Learning ◽  
Energy Savings ◽  
Learning Method ◽  
Solar Pv ◽  
Q Learning ◽  
Pv Systems ◽  
Model Free ◽  
Individual Unit ◽  
Multi Agent ◽  
Micro Grid

In this paper, two solar Photovoltaic (PV) systems are considered; one in the department with capacity of 100 kW and the other in the hostel with capacity of 200 kW. Each one has battery and load. The capital cost and energy savings by conventional methods are compared and it is proved that the energy dependency from grid is reduced in solar micro-grid element, operating in distributed environment. In the smart grid frame work, the grid energy consumption is further reduced by optimal scheduling of the battery, using Reinforcement Learning. Individual unit optimization is done by a model free reinforcement learning method, called Q-Learning and it is compared with distributed operations of solar micro-grid using a Multi Agent Reinforcement Learning method, called Joint Q-Learning. The energy planning is designed according to the prediction of solar PV energy production and observed load pattern of department and the hostel. A simulation model was developed using Python programming.

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