Abstract
This paper presents the preliminary results toward developing a reinforcement learning model of a dynamic solar panel system for maximizing solar energy harvesting. We developed a 2DOF intelligent solar panel system actuated by two servomotors: (i) 1DOF was used to adjust the inclination of the solar panel and (ii) another DOF was used to rotate the solar panel with respect to the vertical axis. The system was designed to rotate the solar panel tracking the rotation (position) of the sun. We conducted two separate simulation studies to analyze the static (stress) and dynamic (stability) characteristics of the rotating solar panel system. We conducted an experiment using the solar panel system to harvest energy and compare the harvested energy between two conditions: (i) the solar panel system was set up facing the sun at the beginning and then remained stationary and (ii) the solar panel system rotated at an arbitrary rotational speed tracking the position of the sun. The simulation results proved satisfactory stress distribution over the solar panel system structure and stability of the system. The experimental results showed that the rotating solar panel was able to produce slightly more energy than the stationary solar panel. To improve the system performance, we proposed a reinforcement learning model to learn the rotational speed of the solar panel system following that of the sun that would maximize energy harvesting. The proposed model can be useful to supply solar power to various applications, especially in remote areas such as hydroponic gardening, irrigation, greenhouses, search and rescue operations, military operations, disaster management, etc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aziz S, Hassan S (2017) On improving the efficiency of a solar panel tracking system. Procedia Manuf 7:218–224
García H, Ramírez C, León N (2014) Innovative solar tracking concept by rotating prism array. Int J Photoenergy 2014:10. Article ID 807159
Baouche F, Abderezzak B, Ladmi A, Arbaoui K, Suciu G, Mihaltan T, Raboaca M, Hudișteanu S, Țurcanu FE, (2022) Design and simulation of a solar tracking system for PV. Appl Sci 12(19). Paper ID 9682
Carballo J, Bonilla J, Berenguel M, Fernandez-Reche J, García G (2019) Machine learning for solar trackers. AIP Conf Proc 2126(1). Paper ID 030012
Abdollahpour M, Golzarian MR, Rohani A, Zarchi HA (2018) Development of a machine vision dual-axis solar tracking system. Sol Energy 169:136–143
Rahman SMM (2021) Machine learning-based cognitive position and force controls for power-assisted human–robot collaborative manipulation. Machines 9(28):1–21
Wheeless S, Rahman SMM (2021) Sensor fusion-based supervised learning approach to developing collaborative manipulation system with variable autonomy. In: Russo D, Ahram T, Karwowski W, Di Bucchianico G, Taiar R (eds) Intelligent human systems integration 2021. ihsi 2021. advances in intelligent systems and computing, vol 1322. Springer, Cham, pp 24–30
Rahman SMM (2021) Image processing-based supervised learning to predict robot intention for multimodal interactions between a virtual human and a social robot. In: Russo D, Ahram T, Karwowski W, Di Bucchianico G, Taiar R (eds) Intelligent human systems integration 2021. ihsi 2021. advances in intelligent systems and computing, vol 1322, pp 533–539, 2021, Springer, Cham
Rahman SMM (2021) Psychophysics-based cognitive reinforcement learning to optimize human-robot interaction in power-assisted object manipulation. In: Russo D, Ahram T, Karwowski W, Di Bucchianico G, Taiar R (eds) Intelligent human systems integration 2021. ihsi 2021. advances in intelligent systems and computing, vol 1322, pp 56–62, Springer, Cham
Singh S, Heard J (2022) Human-aware reinforcement learning for adaptive human robot teaming. In: 2022 17th ACM/IEEE international conference on human-robot interaction (HRI), Sapporo, Japan, pp 1049–1052
Dong L, He Z, Song C, Sun C (2023) A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures. J Syst Eng Electron 34(2):439–459
Mu Y, Li Y, Lin K, Deng K, Liu Q (2022) Battery management for warehouse robots via average-reward reinforcement learning. In: 2022 IEEE international conference on robotics and biomimetics (ROBIO), Jinghong, China, pp 253–258
Mueangprasert M, Chermprayong P, Boonlong K (2023) Robot arm movement control by model-based reinforcement learning using machine learning regression techniques and particle swarm optimization. In: 2023 third international symposium on instrumentation, control, artificial intelligence, and robotics (ICA-SYMP), Bangkok, Thailand, pp 83–86
Kheawkhem P, Khuankrue I (2022) Study on deep reinforcement learning for mobile robots flocking control in certainty situations. In: 2022 19th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), Prachuap Khiri Khan, Thailand, pp 1–4
Rahman SMM, Ikeura R (2016) Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation. J Inf Sci Eng 32(5):1325–1344
Acknowledgements
This paper is based partly on a senior capstone design project performed by three mechanical engineering senior students in Fall 2022 as part of the course ME 440W and modified in Spring 2023 as part of the courses ME 450 and ME 355 at Penn State Scranton under the instruction and guidance of the author. The author is thankful to the senior students who worked on this project. The author is also thankful to the directorate of academic affairs (DAA) at Penn State Scranton for financially supporting the project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rahman, S.M.M. (2024). A Reinforcement Learning Model of a Dynamic Solar Panel System for Maximum Energy Harvesting. In: Nagar, A.K., Jat, D.S., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-99-8111-3_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-8111-3_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8110-6
Online ISBN: 978-981-99-8111-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)