Publications:Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques
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"Byttner, Stefan (stefan), Associate Professor (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))Bouguelia, Mohamed-Rafik (mohbou), Assistant Professor (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))Nowaczyk, Sławomir (slanow), Associate Professor (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))" cannot be used as a page name in this wiki.
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|Title||Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques|
|Abstract||The core of many typical large-scale industrial infrastructure consists of hundreds or thousands of systems that are similar in their basic design and purpose. For instance, District Heating (DH) utilities rely on a large network of substations to deliver heat to their customers. Similarly, a factory may require a large fleet of specialized robots for manufacturing a certain product. Monitoring these systems is important for maintaining the overall efficiency of industrial operations by detecting various problems due to faults and misconfiguration. However, this can be challenging since a well-understood prior model for each system is rarely available. In most cases, each system in a fleet or network is fitted with a set of sensors to measure its state at different time intervals. Typically, a data-driven model for each system can be used for their monitoring. However, not all factors that can possibly influence the operations of each system in a fleet or network has an associated sensor. Moreover, sufficient instances of normal, atypical and faulty behavior are rarely available to train such a model. These issues can impede the effectiveness of a system level data-driven model. Alternatively, it can be assumed that since all the systems in a fleet or network are working on a similar task, they should all behave in a homogeneous manner. Any system that behaves differently from the majority is then considered as an outlier. This is referred to as the global model at the fleet or network level. While the approach is simple, it is less effective in the presence of non-stationary working conditions. Hence, both system level and global modeling approaches have their limitations. This thesis investigates system level and fleet or network level (global) models for large-scale monitoring, and proposes an alternative way which is referred to as a reference-group based approach. Herein, the operational monitoring of each system, referred to as a target, is delegated to a reference-group, which consists of systems experiencing a comparable operating regime along with the target. Thus, the definition of a normal, atypical or faulty operational behavior in a target system is described relative to its reference-group. In this sense, if the target system is not behaving operationally in consort with the systems in its reference-group, then it can be inferred that this is either due to a fault or because of some atypical operation arising at the target system due to its local peculiarities. The application area for these investigations is the large-scale operational monitoring of thermal energy systems: networks of district heating (DH) substations and fleets of heatpumps. The current findings indicate three advantages of a reference-group based approach. The first is that the reference operational behavior of any system in the fleet or network does not need to be predefined. The second is that it provides a basis for what a system’s operational behavior should have been and what it is. In this respect, each system in the reference-group provides an evidence about a particular behavior during a particular time period. This can be very useful when the description of a normal, atypical and faulty operational behavior is not available. The third is that it can detect potential atypical and faulty operational behavior quicker compared to global models of outlier detection at the fleet or network level.|