Digital Transformation in the Water Distribution System based on the Digital Twins Concept
- URL: http://arxiv.org/abs/2412.06694v1
- Date: Mon, 09 Dec 2024 17:40:37 GMT
- Title: Digital Transformation in the Water Distribution System based on the Digital Twins Concept
- Authors: MohammadHossein Homaei, Agustín Javier Di Bartolo, Mar Ávila, Óscar Mogollón-Gutiérrez, Andrés Caro,
- Abstract summary: This paper describes the development of a state-of-the-art DT platform for water distribution systems.
It introduces advanced technologies such as the Internet of Things, Artificial Intelligence, and Machine Learning models.
In this view, the system will contribute to improvements in decision-making capabilities, operational efficiency, and system reliability.
- Score: 0.0
- License:
- Abstract: Digital Twins have emerged as a disruptive technology with great potential; they can enhance WDS by offering real-time monitoring, predictive maintenance, and optimization capabilities. This paper describes the development of a state-of-the-art DT platform for WDS, introducing advanced technologies such as the Internet of Things, Artificial Intelligence, and Machine Learning models. This paper provides insight into the architecture of the proposed platform-CAUCCES-that, informed by both historical and meteorological data, effectively deploys AI/ML models like LSTM networks, Prophet, LightGBM, and XGBoost in trying to predict water consumption patterns. Furthermore, we delve into how optimization in the maintenance of WDS can be achieved by formulating a Constraint Programming problem for scheduling, hence minimizing the operational cost efficiently with reduced environmental impacts. It also focuses on cybersecurity and protection to ensure the integrity and reliability of the DT platform. In this view, the system will contribute to improvements in decision-making capabilities, operational efficiency, and system reliability, with reassurance being drawn from the important role it can play toward sustainable management of water resources.
Related papers
- DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users [52.9870460238443]
We propose a deep reinforcement learning-based blind beamforming technique using a learnable Dolph-Tschebyscheff antenna array.
Our simulation results show that the proposed method can support data rates very close to the best possible values.
arXiv Detail & Related papers (2025-02-03T11:50:43Z) - Secure Resource Allocation via Constrained Deep Reinforcement Learning [49.15061461220109]
We present SARMTO, a framework that balances resource allocation, task offloading, security, and performance.
SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs.
These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments.
arXiv Detail & Related papers (2025-01-20T15:52:43Z) - Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.
By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.
GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
arXiv Detail & Related papers (2024-08-02T10:47:10Z) - Digital Twin-Enhanced Deep Reinforcement Learning for Resource
Management in Networks Slicing [46.65030115953947]
We propose a framework consisting of a digital twin and reinforcement learning agents.
Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment.
We also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data.
arXiv Detail & Related papers (2023-11-28T15:25:14Z) - TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework [58.474610046294856]
Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime.
This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system maintenance actions.
arXiv Detail & Related papers (2023-09-29T02:27:54Z) - Digital Twins and the Future of their Use Enabling Shift Left and Shift Right Cybersecurity Operations [15.061739314361871]
Digital Twins (DTs) optimize operations and monitor performance in Smart Critical Systems (SCS) domains like smart grids and manufacturing.
This vision paper outlines intelligent SDT design through innovative techniques, exploring hybrid intelligence with data-driven and rule-based semantic SDT models.
arXiv Detail & Related papers (2023-09-24T11:20:58Z) - Digital Twins based Day-ahead Integrated Energy System Scheduling under
Load and Renewable Energy Uncertainties [14.946548030861866]
Digital twins (DT) of an integrated energy system (IES) can improve coordinations among various energy converters.
Case studies show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%.
arXiv Detail & Related papers (2021-09-29T13:58:01Z) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.