| 1. |
- Calikus, Ece, 1990-, et al.
(författare)
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A data-driven approach for discovering heat load patterns in district heating
- 2019
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Ingår i: Applied Energy. - Oxford : Elsevier. - 0306-2619 .- 1872-9118. ; 252
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Calikus, Ece, 1990-, et al.
(författare)
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Context Discovery for Anomaly Detection
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Contextual anomaly detection aims at identifying objects that are anomalous only within specific contexts. Most existing methods are limited to a single context defined by user-specified features. While identifying the right context is not trivial in practice, there is often more than just one context in real-world systems under which different anomalies naturally occur. In this work, we introduce ConQuest, a new unsupervised contextual anomaly detection approach that automatically discovers and incorporates multiple contexts useful for revealing contextual anomalies. In ConQuest, we search for relevant contexts by optimizing an unsupervised multi-objective function, where each objective is derived from desired properties of contextual anomaly detection. To effectively balance such (often competing) properties, we use a multi-objective genetic algorithm that returns a Pareto front comprising diverse, non-dominated solutions. Through experiments on various datasets, we show ConQuest outperforms state-of-the-art methods. Further, we showcase the advantage of using multiple objectives over single-objective context discovery strategies and demonstrate the interpretability aspect of ConQuest.
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| 3. |
- Calikus, Ece, 1990-, et al.
(författare)
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Interactive-cosmo : Consensus self-organized models for fault detection with expert feedback
- 2019
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Ingår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019. - New York : Association for Computing Machinery (ACM). - 9781450362962 ; , s. 1-9
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Konferensbidrag (refereegranskat)abstract
- Diagnosing deviations and predicting faults is an important task, especially given recent advances related to Internet of Things. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. One promising approach towards self-monitoring systems is based on the "wisdom of the crowd" idea, where malfunctioning equipments are detected by understanding the similarities and differences in the operation of several alike systems.A fully autonomous fault detection, however, is not possible, since not all deviations or anomalies correspond to faulty behaviors; many can be explained by atypical usage or varying external conditions. In this work, we propose a method which gradually incorporates expert-provided feedback for more accurate self-monitoring. Our idea is to support model adaptation while allowing human feedback to persist over changes in data distribution, such as concept drift. © 2019 Association for Computing Machinery.
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| 4. |
- Calikus, Ece, 1990-, et al.
(författare)
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No free lunch but a cheaper supper : A general framework for streaming anomaly detection
- 2020
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Ingår i: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.
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| 5. |
- Calikus, Ece, 1990-, et al.
(författare)
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Ranking Abnormal Substations by Power Signature Dispersion
- 2018
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Ingår i: Energy Procedia. - Amsterdam : Elsevier. - 1876-6102. ; 149, s. 345-353
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Tidskriftsartikel (refereegranskat)abstract
- The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.
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| 6. |
- Calikus, Ece, 1990-
(författare)
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Self-Monitoring using Joint Human-Machine Learning : Algorithms and Applications
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 7. |
- Calikus, Ece, 1990-
(författare)
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Together We Learn More : Algorithms and Applications for User-Centric Anomaly Detection
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 8. |
- Calikus, Ece, 1990-, et al.
(författare)
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Wisdom of the contexts : active ensemble learning for contextual anomaly detection
- 2022
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Ingår i: Data mining and knowledge discovery. - New York : Springer-Verlag New York. - 1384-5810 .- 1573-756X. ; 36, s. 2410-2458
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Tidskriftsartikel (refereegranskat)abstract
- In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we propose a novel approach, called WisCon (Wisdom of the Contexts), to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. We estimate the importance of each context using an active learning approach with a novel query strategy. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual, and non-contextual anomaly detectors) on 18 datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary. © 2022, The Author(s).
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| 9. |
- Mbiydzenyuy, Gideon, et al.
(författare)
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Opportunities for Machine Learning in District Heating
- 2021
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Ingår i: Applied Sciences. - Basel : MDPI. - 2076-3417. ; 11:13
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Tidskriftsartikel (refereegranskat)abstract
- The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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| 10. |
- Nowaczyk, Sławomir, 1978-, et al.
(författare)
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Monitoring equipment operation through model and event discovery
- 2018
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Ingår i: Intelligent Data Engineering and Automated Learning – IDEAL 2018. - Cham : Springer. - 9783030034955 - 9783030034962 ; , s. 41-53
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Konferensbidrag (refereegranskat)abstract
- Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group. We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system. © Springer Nature Switzerland AG 2018.
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