Deep Reinforcement Learning for Artificial Upwelling Energy Management
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910427834744832 |
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| author | Zhang, Yiyuan Fan, Wei |
| author_facet | Zhang, Yiyuan Fan, Wei |
| contents | The potential of artificial upwelling (AU) as a means of lifting nutrient-rich bottom water to the surface, stimulating seaweed growth, and consequently enhancing ocean carbon sequestration, has been gaining increasing attention in recent years. This has led to the development of the first solar-powered and air-lifted AU system (AUS) in China. However, efficient scheduling of air injection systems in complex marine environments remains a crucial challenge in operating AUS, as it holds the potential to significantly improve energy efficiency. To tackle this challenge, we propose a novel energy management approach that utilizes deep reinforcement learning (DRL) algorithm to develop efficient strategies for operating AUS. Specifically, we formulate the problem of maximizing the energy efficiency of AUS as a Markov decision process and integrate the quantile network in distributional reinforcement learning (QR-DQN) with the deep dueling network to solve it. Through extensive simulations, we evaluate the performance of our algorithm and demonstrate its superior effectiveness over traditional rule-based approaches and other DRL algorithms in reducing energy wastage while ensuring the stable and efficient operation of AUS. Our findings suggest that a DRL-based approach offers a promising way to improve the energy efficiency of AUS and enhance the sustainability of seaweed cultivation and carbon sequestration in the ocean. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_10199 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Artificial Upwelling Energy Management Zhang, Yiyuan Fan, Wei Machine Learning Artificial Intelligence The potential of artificial upwelling (AU) as a means of lifting nutrient-rich bottom water to the surface, stimulating seaweed growth, and consequently enhancing ocean carbon sequestration, has been gaining increasing attention in recent years. This has led to the development of the first solar-powered and air-lifted AU system (AUS) in China. However, efficient scheduling of air injection systems in complex marine environments remains a crucial challenge in operating AUS, as it holds the potential to significantly improve energy efficiency. To tackle this challenge, we propose a novel energy management approach that utilizes deep reinforcement learning (DRL) algorithm to develop efficient strategies for operating AUS. Specifically, we formulate the problem of maximizing the energy efficiency of AUS as a Markov decision process and integrate the quantile network in distributional reinforcement learning (QR-DQN) with the deep dueling network to solve it. Through extensive simulations, we evaluate the performance of our algorithm and demonstrate its superior effectiveness over traditional rule-based approaches and other DRL algorithms in reducing energy wastage while ensuring the stable and efficient operation of AUS. Our findings suggest that a DRL-based approach offers a promising way to improve the energy efficiency of AUS and enhance the sustainability of seaweed cultivation and carbon sequestration in the ocean. |
| title | Deep Reinforcement Learning for Artificial Upwelling Energy Management |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.10199 |