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Abstract: In this work, we propose the use of a reinforcement learning (RL) agent for the control of a cascade hydropower plant system. Generally, this job is handled by power plant dispatchers who manually adjust power plant electricity production to meet the changing demand set by energy traders. This work explores the more fundamental problem with the cascade hydropower plant operation of flow control for power production in a highly nonlinear setting on a data-based digital twin. Using deep deterministic policy gradient (DDPG), twin delayed DDPG (TD3), soft actor-critic (SAC), and proximal policy optimization (PPO) algorithms, we can generalize the characteristics of the system and determine the human dispatcher level of control of the entire system of eight hydropower plants on the river Drava in Slovenia. The creation of an RL agent that makes decisions similar to a human dispatcher is not only interesting in terms of control but also in terms of long-term decision-making analysis in an ever-changing energy portfolio. The specific novelty of this work is in training an RL agent on an accurate testing environment of eight real-world cascade hydropower plants on the river Drava in Slovenia and comparing the agent’s performance to human dispatchers. The results show that the RL agent’s absolute mean error of 7.64 MW is comparable to the general human dispatcher’s absolute mean error of 5.8 MW at a peak installed power of 591.95 MW.
Keywords: cascade hydropower, reinforcement learning, digital twin
Published in DKUM: 17.10.2025; Views: 0; Downloads: 6
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Abstract: The Bled eConference, organised by the University of Maribor, Faculty of Organizational Sciences, has been shaping electronic interactions since 1988. The theme of the 38th conference is "Empowering transformation: Shaping digital futures for all". The central theme highlights the need for a digital transformation that is focused on the well-being of individuals and society as a whole. It emphasizes the importance of inclusive and sustainable development that enables equal participation of diverse stakeholders in shaping a digital future for all—one that is grounded in ethical, social, and sustainable principles. Within this framework, digital technologies are viewed as enablers for the transformation of existing systems and solutions in a human-centered way. The goal is to foster innovation, strengthen resilience, and promote long-term societal and economic progress. The papers in this conference proceedings explore a range of topics including the opportunities and challenges of the twin transition, emerging technologies, artificial intelligence and data science, decision analytics for business and societal changes, digital innovation and business models, restructured work and the future workplace, digital health, digital ethics, digital education, smart sustainable cities, digital consumers, and the digital transformation of the public sector.
Keywords: resilience, digital transformation, digital innovation, twin transition, sustainability, digital transition, green transition, designing digital future
Published in DKUM: 09.06.2025; Views: 0; Downloads: 57
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Abstract: A digital twin is a virtual model to reflect a physical object and helps it by making proper decisions. The decision-making process is based on the same input data that the simulated physical object has access to. Due to exploiting artificial intelligence, the decision-making process of the digital twin is more sophisticated than that of the physical object. In this study, the digital twin is applied to the sports training domain, where it addresses those questions that have arisen during the implementation of interval cycling training sessions. Thus, the digital twin runs on a mobile device (i.e., the Raspberry Pi platform), with which a cycle is equipped and demonstrates user-friendliness, robustness, reliability, and accuracy. The interval training sessions are transferred to the mobile device in the form of the domain-specific language EasyTrain, ensuring higher expressive power and ease of use. During the implementation, the digital twin advises the athlete with predicted information obtained by a sophisticated prediction model via a screen. The results of a huge experimental work showed that the difference in the average efficiency of the interval training implementation between the two cyclists that performed the experiments is prominent, as the efficiency of the professional training surpassed 90%, while the amateur training efficiency barely achieved 70%.
Keywords: digital twin, interval sports training, healthcare, domain-specific language, cycling
Published in DKUM: 23.08.2024; Views: 119; Downloads: 13
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Abstract: Logistics and transport are major sources of energy consumption that still rely heavily on fossil fuels. Especially in the freight sector, finding means to optimise fuel consumption and energy efficiency are increasingly important. Digital twins’ adaptation in logistics and transport is not as frequent as in production, but their implementation potential is immense. This technology can replicate real environments, allowing verification of various scenarios without real-life application, leading to optimal implementation outcome faster and more efficiently. This paper aims to research digital twins’ use in logistics and transport, focusing on digital twins’ potential and effects to optimise energy consumption. Firstly, previous research on digital twins in specified fields was identified, followed by a quantitative literature review. The latter focused on codifying the selected publications based on criteria such as modality, specific environment, energy consumption, etc. Furthermore, a qualitative overview of 57 relevant publications on digital twins’ use in logistics and transport was made. The main contribution of this paper is the systematic overview of digital twins from the viewpoint of energy optimisation in one of the most energy-dependent sectors. This gives researchers a good starting point for further research and digital twins’ practical implementation cases.
Keywords: logistics, transport, digital twin, energy consumption, optimisation, literature review, bibliometrics
Published in DKUM: 22.04.2024; Views: 129; Downloads: 39
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Abstract: Human–robot collaborative systems bring several benefits in using human and robot capabilities simultaneously. One of the critical questions is the impact of these systems on production process efficiency. The search for high-level efficiency is severely dependent on collaborative robot characteristics and motion parameters, and the ability of humans to adjust to changing circumstances. Therefore, our research analyzes the effect of the changing collaborative robot motion parameters, acoustic parameters and visual factors in a specific assembly operation, where efficiency is measured through operation times. To conduct our study, we designed a digital twin-based model and a laboratory environment experiment in the form of a collaborative workplace. The results show that changing the motion, acoustic and visual parameters of the collaborative workplace impact the assembly process efficiency significantly.
Keywords: collaborative robot, collaborative workplace, digital twin, assembly operation, efficiency
Published in DKUM: 26.02.2024; Views: 301; Downloads: 31
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