Technische Hochschule Würzburg-Schweinfurt Publikationsserver OPUS
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How teaching conceptual modeling to robotics students changes their perception of software engineering
No full textThe State of the Universe and the Union: A Look Back in Anguish on Last Week through a Migratory Burning Glass
No full textDie Soziale Arbeit und das Digitale - Menschenrechtliche und professionsethische Perspektiven
No full textHome-based family caregiver-delivered music and reading interventions for people living with dementia (HOMESIDE trial): an international randomised controlled trial
No full textFuture role and economic benefits of hydrogen and synthetic energy carriers in Germany: a review of long-term energy scenarios
No full textDetermining the development of Germany’s energy system is the subject of a series of studies. Since their results play a significant role in the political energy debate for understanding the role of hydrogen and synthetic energy carriers, a better discussion is needed. This article provides an assessment of published transition pathways for Germany to assess the role and advantages of hydrogen-based carriers. Twelve energy studies including 37 scenarios for the years 2030 and 2050 were evaluated. Despite the variations, the carrier will play an important role. While their deployment is expected to have only started by 2030 with a mean demand of 91 TWh/a (4% of the final energy demand) in Germany, they will be an essential part by 2050 with a mean demand of 480 TWh/a (24%). The outcome of the scenarios depends on the chosen methods and assumptions. A moderately positive correlation (0.53) between the decarbonisation targets and the share of hydrogen-based carriers in final energy demand underlines the relevance for reaching the climate targets. Additionally, value creation effects of about 16 billion EUR/a in 2050 can be expected for hydrogen-based carriers. Hydrogen is expected to be produced domestically while synthetic fuels are projected to be mostly imported
Data Center HVAC Control Harnessing Flexibility Potential via Real-Time Pricing Cost Optimization Using Reinforcement Learning
No full textWith increasing electricity prices, cost savings through load shifting are becoming increasingly important for energy end users. While dynamic pricing encourages customers to shift demand to low price periods, the nonstationary and highly volatile nature of electricity prices poses a significant challenge to energy management systems. In this article, we investigate the flexibility potential of data centers by optimizing heating, ventilation, and air conditioning systems with a general model-free reinforcement learning (RL) approach. Since the soft actor-critic algorithm with feedforward networks did not work satisfactorily in this scenario, we propose instead a parameterization with a recurrent neural network architecture to successfully handle spot-market price data. The past is encoded into a hidden state, which provides a way to learn the temporal dependencies in the observations and highly volatile rewards. The proposed method is then evaluated in experiments on a simulated data center. Considering real temperature and price signals over multiple years, the results show a cost reduction compared to a proportional, integral and derivative controller while maintaining the temperature of the data center within the desired operating ranges. In this context, this work demonstrates an innovative and applicable RL approach that incorporates complex economic objectives into agent decision-making. The proposed control method can be integrated into various Internet of Things-based smart building solutions for energy management
