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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: 2019-01-08Bibliographically approved

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Farouq, ShirazByttner, Stefan

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