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Towards understanding district heating substation behavior using robust first difference regression
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Öresundskraft AB, Ängelholm, Sweden.
2018 (English)In: Energy Procedia, Amsterdam: Elsevier, 2018, Vol. 149, p. 236-245Conference paper, Published paper (Refereed)
Abstract [en]

The behavior of a district heating (DH) substation has a social and operational context. The social context comes from its general usage pattern and personal requirements of building inhabitants. The operational context comes from its configuration settings which considers both the weather conditions and social requirements. The parameter estimating thermal energy demand response with respect to change in outdoor temperature conditions along with the strength of the relationship between these variables are two important measures of operational efficiency of a substation. In practice, they can be estimated using a regression model where the slope parameter measures the average response and R2 measures the strength of the relationship. These measures are also important from a monitoring perspective. However, factors related to the social context of a building and the presence of unexplained outliers can make the estimation of these measures a challenging task. Social context of a data point in DH, in many cases appears as an outlier. Data efficiency is also required if these measures are to be estimated in a timely manner. Under these circumstances, methods that can isolate and reduce the effect of outliers in a principled and data efficient manner are required. We therefore propose to use Huber regression, a robust method based on M-estimator type loss function. This method can not only identify possible outliers present in the data of each substation but also reduce their effect on the estimated slope parameter. Moreover, substations that are comparable according to certain criteria, for instance, those with almost identical energy demand levels, should have relatively similar slopes. This provides an opportunity to observe deviating substations under the assumption that comparable substations should show homogeneity in their behavior. Furthermore, the slope parameter can be compared across time to observe if the dynamics of a substation has changed. Our analysis shows that Huber regression in combination with ordinary least squares can provide reliable estimates on the operational efficiency of DH substations. © 2018 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018. Vol. 149, p. 236-245
Series
Energy Procedia, E-ISSN 1876-6102 ; 149
Keywords [en]
district heating, energy demand response, outliers, robust regression, substation control, Energy management, Regression analysis, Statistics, Energy demands, Heating substations, Operational efficiencies, Ordinary least squares, Parameter estimating, Robust regressions, Substation controls, Parameter estimation
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:hh:diva-38729DOI: 10.1016/j.egypro.2018.08.188Scopus ID: 2-s2.0-85054085009OAI: oai:DiVA.org:hh-38729DiVA, id: diva2:1276514
Conference
16th International Symposium on District Heating and Cooling, DHC 2018, 9-12 September, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-11-16Bibliographically approved
In thesis
1. Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques
Open this publication in new window or tab >>Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2019. p. 65
Series
Halmstad University Dissertations ; 65
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40964 (URN)978-91-88749-38-3 (ISBN)978-91-88749-39-0 (ISBN)
Presentation
2019-11-26, O125, O building, Linjegatan 12, Halmstad, 13:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2019-11-18 Created: 2019-11-16 Last updated: 2019-11-18Bibliographically approved

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Farouq, ShirazByttner, Stefan

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