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A data-driven approach for discovering heat load patterns in district heating
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-6249-4144
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-3495-2961
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS). Öresundskraft, Helsingborg, Sweden.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 252, artikkel-id 113409Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers’ heat-use habits. © 2019 Calikus et al. Published by Elsevier Ltd.

sted, utgiver, år, opplag, sider
Oxford: Elsevier, 2019. Vol. 252, artikkel-id 113409
Emneord [en]
District heating, Energy efficiency, Heat load patterns, Clustering, Abnormal heat use
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-40907DOI: 10.1016/j.apenergy.2019.113409ISI: 000497968000013Scopus ID: 2-s2.0-85066961984OAI: oai:DiVA.org:hh-40907DiVA, id: diva2:1369639
Forskningsfinansiär
Knowledge Foundation, 20160103Tilgjengelig fra: 2019-11-12 Laget: 2019-11-12 Sist oppdatert: 2022-02-22bibliografisk kontrollert
Inngår i avhandling
1. Self-Monitoring using Joint Human-Machine Learning: Algorithms and Applications
Åpne denne publikasjonen i ny fane eller vindu >>Self-Monitoring using Joint Human-Machine Learning: Algorithms and Applications
2020 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The ability to diagnose deviations and predict faults effectively is an important task in various industrial domains for minimizing costs and productivity loss and also conserving environmental resources. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. Automated data-driven solutions are needed for continuous monitoring of complex systems over time. On the other hand, domain expertise plays a significant role in developing, evaluating, and improving diagnostics and monitoring functions. Therefore, automatically derived solutions must be able to interact with domain experts by taking advantage of available a priori knowledge and by incorporating their feedback into the learning process.

This thesis and appended papers tackle the problem of generating a real-world self-monitoring system for continuous monitoring of machines and operations by developing algorithms that can learn data streams and their relations over time and detect anomalies using joint-human machine learning. Throughout this thesis, we have described a number of different approaches, each designed for the needs of a self-monitoring system, and have composed these methods into a coherent framework. More specifically, we presented a two-layer meta-framework, in which the first layer was concerned with learning appropriate data representations and detectinganomalies in an unsupervised fashion, and the second layer aimed at interactively exploiting available expert knowledge in a joint human-machine learning fashion.

Furthermore, district heating has been the focus of this thesis as the application domain with the goal of automatically detecting faults and anomalies by comparing heat demands among different groups of customers. We applied and enriched different methods on this domain, which then contributed to the development and improvement of the meta-framework. The contributions that result from the studies included in this work can be summarized into four categories: (1) exploring different data representations that are suitable for the self-monitoring task based on data characteristics and domain knowledge, (2) discovering patterns and groups in data that describe normal behavior of the monitored system/systems, (3) implementing methods to successfully discriminate anomalies from the normal behavior, and (4) incorporating domain knowledge and expert feedback into self-monitoring.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2020. s. 45
Serie
Halmstad University Dissertations ; 69
Emneord
self-monitoring, anomaly detection, machine learning
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-41421 (URN)978-91-88749-47-5 (ISBN)978-91-88749-46-8 (ISBN)
Presentation
2020-02-25, J102 Wigforssalen, Kristian IV:s väg 3, Halmstad, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation, 20160103
Tilgjengelig fra: 2020-01-31 Laget: 2020-01-29 Sist oppdatert: 2020-01-31bibliografisk kontrollert
2. Together We Learn More: Algorithms and Applications for User-Centric Anomaly Detection
Åpne denne publikasjonen i ny fane eller vindu >>Together We Learn More: Algorithms and Applications for User-Centric Anomaly Detection
2022 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Anomaly detection is the problem of identifying data points or patterns that do not conform to normal behavior. Anomalies in data often correspond to important and actionable information such as frauds in financial applications, faults in production units, intrusions in computer systems, and serious diseases in patient records. One of the fundamental challenges of anomaly detection is that the exact notion of anomaly is subjective and varies greatly in different applications and domains. This makes distinguishing anomalies that match with the end-user's expectations from other observations difficult. As a result, anomaly detectors produce many false alarms that do not correspond to semantically meaningful anomalies for the analyst. 

Humans can help, in different ways, to bridge this gap between detected anomalies and ''anomalies-of-interest'': by giving clues on features more likely to reveal interesting anomalies or providing feedback to separate them from irrelevant ones. However, it is not realistic to assume a human to easily provide feedback without explaining why the algorithm classifies a certain sample as an anomaly. Interpretability of results is crucial for an analyst to be able to investigate the candidate anomaly and decide whether it is actually interesting or not. 

In this thesis, we take a step forward to improve the practical use of anomaly detection in real-life by leveraging human-algorithm collaboration. This thesis and appended papers study the problem of formulating and implementing algorithms for user-centric anomaly detection-- a setting in which people analyze, interpret, and learn from the detector's results, as well as provide domain knowledge or feedback. Throughout this thesis, we have described a number of diverse approaches, each addressing different challenges and needs of user-centric anomaly detection in the real world, and combined these methods into a coherent framework. By conducting different studies, this thesis finds that a comprehensive approach incorporating human knowledge and providing interpretable results can lead to more effective and practical anomaly detection and more successful real-world applications. The major contributions that result from the studies included in this work and led the above conclusion can be summarized into five categories: (1) exploring different data representations that are suitable for anomaly detection based on data characteristics and domain knowledge, (2) discovering patterns and groups in data that describe normal behavior in the current application, (3) implementing a generic and extensible framework enabling use-case-specific detectors suitable for different scenarios, (4) incorporating domain knowledge and expert feedback into anomaly detection, and (5) producing interpretable detection results that support end-users in understanding and validating the anomalies. 

sted, utgiver, år, opplag, sider
Halmstad University Press, 2022. s. 211
Serie
Halmstad University Dissertations ; 9
Emneord
data mining, machine learning, anomaly detection
HSV kategori
Forskningsprogram
Smarta städer och samhällen
Identifikatorer
urn:nbn:se:hh:diva-46404 (URN)978-91-88749-87-1 (ISBN)978-91-88749-88-8 (ISBN)
Disputas
2022-03-22, Visionen (Halda), Kristian IV:s väg 3, Halmstad, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2022-02-25 Laget: 2022-02-22 Sist oppdatert: 2022-02-25bibliografisk kontrollert

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Calikus, EceNowaczyk, SławomirPinheiro Sant'Anna, AnitaGadd, HenrikWerner, Sven

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