bookmarks (hide)61
- Abstract page for arXiv paper 2501.19234: Hourly Short Term Load Forecasting for Residential Buildings and Energy Communities
- The constantly increasing electricity and energy demand in residential buildings, as well as the need for higher absorption rates of renewable sources of energy, demand for an increased flexibility at the end-users. This need is further reinforced by the rising numbers of residential Photovoltaic (PV) and battery-storage systems. In this case, flexibility can be viewed as the excess energy that can be charged to or discharged from a battery, in response to a group objective of several such battery-storage systems (aggregation). One such group objective considered in this paper includes marketing flexibility (charging or discharging) to the Day-ahead (DA) spot market, which can provide both a) financial incentives to the owners of such systems, and b) an increase in the overall absorption rates of renewable energy. The responsible agent for marketing and offering such flexibility, herein aggregator, is directly controlling the participating batteries, in exchange to some financial compensation of the owners of these batteries. In this paper, we present an optimization framework that allows the aggregator to optimally exchange the available flexibility to the DA market. The proposed scheme is based upon a reinforcement-learning approach, according to which the aggregator learns through time an optimal policy for bidding flexibility to the DA market. By design, the proposed scheme is flexible enough to accommodate the possibility of erroneous forecasts (of weather, load or electricity price). Finally, we evaluate our approach on real-world data collected from currently installed battery-storage systems in Upper Austria.
- Predictive maintenance is a crucial strategy in smart industries and plays an important role in small and medium-sized enterprises (SMEs) to reduce the unexpected breakdown. Machine failures are due to unexpected events or anomalies in the system. Different anomaly...
- Despite a recent surge of research interest in privacy and transferrable deep learning, optimizing the tradeoff between privacy requirements and performanc
- Unlike the controlled conditions of AI system engineering laboratories, where adversarial vulnerabilities under specific threat models can be examined in isolation, in practical environments, such vulnerabilities are commonly intertwined with additional risks,...
- Identifying possible use cases for quantum computers is important to evaluate the potential. This work-in-progress paper explores their potential for addressing the short-term generation scheduling (STGS) problem in hydropower plants. By working towards reformulating...
- Abstract page for arXiv paper 2503.08502: The Space Between: On Folding, Symmetries and Sampling
- Abstract page for arXiv paper 2501.07952: Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
- Abstract page for arXiv paper 2411.19713: CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures
- Abstract page for arXiv paper 2411.10152: Causal Time-Series Synchronization for Multi-Dimensional Forecasting
- Code completion is a very important feature of modern integrated development environments. Research has been done for years to improve code completion systems for general-purpose languages. However, only little literature can be found for (AI-based) code completion...
- We introduce a novel copy-protection method for industrial control software. With our method, a program executes correctly only on its target hardware and behaves differently on other machines. The hardware-software binding is based on Physically Unclonable...
- Human-AI collaboration in industrial manufacturing promises to overcome current limitations by combining the flexibility of human intelligence and the scaling and processing capabilities of machine intelligence. To ensure effective collaboration between human and AI...
- Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of...
publications (hide)83
- ACM Transactions on Quantum Computing, 3 (3): 1--17 (June 2022)
- IEEE Transactions on Fuzzy Systems, 31 (8): 2617-2631 (August 2023)
- IEEE Access, (2023)
- Proceedings of the 15th International Working Conference on Variability Modelling of Software-Intensive Systems, ACM, (February 2021)
- 2018 Thirteenth International Conference on Digital Information Management (ICDIM), page 148-155. (September 2018)
- 35th International Conference on Scientific and Statistical Database Management, ACM, (July 2023)
- Database and Expert Systems Applications - DEXA 2023 Workshops, page 3--10. Cham, Springer Nature Switzerland, (2023)
- 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), page 1-8. (September 2022)
- (2022)cite arxiv:2204.05765.
- (2022)cite arxiv:2211.01645.
- (2023)cite arxiv:2304.01300.
- Proceedings of the Companion Conference on Genetic and Evolutionary Computation, ACM, (July 2023)
- Annals of Mathematics and Artificial Intelligence, 89 (3): 333--369 (Mar 1, 2021)
- Database and Expert Systems Applications - DEXA 2022 Workshops, page 22--31. Cham, Springer International Publishing, (2022)
- Frontiers in Chemical Engineering, (June 2021)
- Rigorous State-Based Methods, Springer Nature Switzerland, (2023)
- Model and Data Engineering, Springer Nature Switzerland, (November 2022)
- Frontiers in Computer Science, (April 2022)
- Data & Knowledge Engineering, (2013)
- Theoretical Computer Science, (2016)
