@article{FalkElGhayedvandeSandetal.2023, author = {Falk, Constantin and El Ghayed, Tarek and van de Sand, Ron and Reiff-Stephan, J{\"o}rg}, title = {A Data-Driven Approach Towards the Application of Reinforcement Learning Based HVAC Control}, series = {Journal of the Nigerian Society of Physical Sciences}, volume = {5}, journal = {Journal of the Nigerian Society of Physical Sciences}, number = {1}, publisher = {Department of Physics, Federal University Lafia Nasarawa State, Nigeria ; Nigerian Society of Physical Sciences}, issn = {2714-4704}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17066}, year = {2023}, abstract = {Refrigeration applications consume a significant share of total electricity demand, with a high indirect impact on global warming through greenhouse gas emissions. Modern technology can help reduce the high power consumption and optimize the cooling control. This paper presents a case study of machine-learning for controlling a commercial refrigeration system. In particular, an approach to reinforcement learning is implemented, trained and validated utilizing a model of a real chiller plant. The reinforcement-learning controller learns to operate the plant based on its interactions with the modeled environment. The validation demonstrates the functionality of the approach, saving around 7\% of the energy demand of the reference control. Limitations of the approach were identified in the discretization of the real environment and further model-based simplifications and should be addressed in future research.}, language = {en} } @inproceedings{FalkvandeSandCorasanitietal.2021, author = {Falk, Constantin and van de Sand, Ron and Corasaniti, Sandra and Reiff-Stephan, J{\"o}rg}, title = {A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers}, series = {2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)}, booktitle = {2nd German-West African Conference on Sustainable, Renewable Energy Systems (SusRES2021)}, publisher = {TIB Open Publishing}, address = {Hannover}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-14970}, pages = {165 -- 174}, year = {2021}, abstract = {Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.}, language = {en} }