hh.sePublications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Calikus, Ece
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gadd, Henrik
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS). Öresundskraft, Helsingborg, Sweden.
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    A data-driven approach for discovering heat load patterns in district heating2019In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 252, article id 113409Article in journal (Refereed)
    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.

  • 2.
    Farouq, Shiraz
    et al.
    Halmstad University, School of Information Technology.
    Byttner, Stefan
    Halmstad University, School of Information Technology.
    Bouguelia, Mohamed-Rafik
    Halmstad University, School of Information Technology.
    Gadd, Henrik
    Halmstad University, School of Business, Innovation and Sustainability.
    A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating2022In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 201, article id 116864Article in journal (Refereed)
    Abstract [en]

    The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and subfleet-level models to observe the behavior of a target unit. However, such infrastructure has to confront several challenges. First, from an anomaly detection perspective of monitoring a target unit, unit-level and subfleet-level models can give different information about the nature of an anomaly, and which approach or level model is appropriate is not always clear. Second, in the absence of well-understood prior models of unit and subfleet behavior, the choice of a base model at their respective levels, especially in an online/streaming setting, may not be clear. Third, managing false alarms is a major problem. To deal with these challenges, we proposed to rely on the conformal anomaly detection framework. In addition, an ensemble approach was deployed to mitigate the knowledge gap in understanding the underlying data-generating process at the unit and subfleet levels. Therefore, to monitor the behavior of a target unit, a unit-level ensemble model (ULEM) and a subfleet-level ensemble model (SLEM) were constructed, where each member of the respective ensemble is based on a conformal anomaly detector (CAD). However, since the information obtained by these two ensemble models through their p-values may not always agree, a combined ensemble model (CEM) was proposed. The results are based on real-world operational data obtained from district heating (DH) substations. Here, it was observed that CEM reduces the overall false alarms compared to ULEM or SLEM, albeit at the cost of some detection delay. The analysis demonstrated the advantages and limitations of ULEM, SLEM, and CEM. Furthermore, discords obtained from the state-of-the-art matrix-profile (MP) method and the combined calibration scores obtained from ULEM and SLEM were compared in an offline setting. Here, it was observed that SLEM achieved a better overall precision and detection delay. Finally, the different components related to ULEM, SLEM, and CEM were put together into what we refer to as TRANTOR: a conformal anomaly detection based industrial fleet monitoring framework. The proposed framework is expected to enable fleet operators in various domains to improve their monitoring infrastructure by efficiently detecting anomalous behavior and controlling false alarms at the target units. © 2022

  • 3.
    Farouq, Shiraz
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bouguelia, Mohamed-Rafik
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gadd, Henrik
    Halmstad University, School of Business, Innovation and Sustainability, The Rydberg Laboratory for Applied Sciences (RLAS).
    Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets2021In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 462, p. 591-606Article in journal (Refereed)
    Abstract [en]

    We considered the case of monitoring a large fleet where heterogeneity in the operational behavior among its constituent units (i.e., systems or machines) is non-negligible, and no labeled data is available. Each unit in the fleet, referred to as a target, is tracked by its sub-fleet. A conformal sub-fleet (CSF) is a set of units that act as a proxy for the normal operational behavior of a target unit by relying on the Mondrian conformal anomaly detection framework. Two approaches, the k-nearest neighbors and conformal clustering, were investigated for constructing such a sub-fleet by formulating a stability criterion. Moreover, it is important to discover the sub-sequence of events that describes an anomalous behavior in a target unit. Hence, we proposed to extract such sub-sequences for further investigation without pre-specifying their length. We refer to it as a conformal anomaly sequence (CAS). Furthermore, different nonconformity measures were evaluated for their efficiency, i.e., their ability to detect anomalous behavior in a target unit, based on the length of the observed CAS and the S-criterion value. The CSF approach was evaluated in the context of monitoring district heating substations. Anomalous behavior sub-sequences were corroborated with the domain expert leading to the conclusion that the proposed approach has the potential to be useful for both diagnostic and knowledge extraction purposes, especially in domains where labeled data is not available or hard to obtain. © 2021

  • 4.
    Gadd, Henrik
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energiteknik.
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energiteknik.
    Achieving low return temperature from district heating substations2014In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 136, p. 59-67Article in journal (Refereed)
    Abstract [en]

    District heating systems contribute with low primary energy supply in the energy system by providing heat from heat assets like combined heat and power, waste incineration, geothermal heat, wood waste, and industrial excess heat. These heat assets would otherwise be wasted or not used. Still, there are several reasons to use these assets as efficiently as possible, i.e., ability to compete, further reduced use of primary energy resources, and less environmental impact. Low supply and return temperatures in the distribution networks are important operational factors for obtaining an efficient district heating system. In order to achieve low return temperatures, customer substations and secondary heating systems must perform without temperature faults. In future fourth generation district heating systems, lower distribution temperatures will be required. To be able to have well-performing substations and customer secondary systems, continuous commissioning will be necessary to be able to detect temperature faults without any delays. It is also of great importance to be able to have quality control of eliminated faults. Automatic meter reading systems, recently introduced into district heating systems, have paved the way for developing new methods to be used in continuous commissioning of substations. This paper presents a novel method using the temperature difference signature for temperature difference fault detection and quality assurance of eliminated faults. Annual hourly datasets from 140 substations have been analysed for temperature difference faults. From these 140 substations, 14 were identified with temperature difference appearing or eliminated during the analysed year. Nine appeared during the year, indicating an annual temperature difference fault frequency of more than 6%. © 2014 The Authors.

  • 5.
    Gadd, Henrik
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energiteknik.
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energiteknik.
    Daily Heat Load Variation in Swedish District Heating Systems2010In: 12th International Symposium on District Heating and Cooling, Tallinn: Tallinn University of Technology , 2010, p. 199-201Conference paper (Refereed)
    Abstract [en]

    If daily heat load variations could be eliminated in district heating-systems, it would make the operation of the district heating system less costly and more competitive . There would be several advantages in the operation such as:

    • Less use of expensive peak load power where often expensive fuels are used.
    • Less need for peak load power capacity.
    • Easier to optimize the operation that leads to higher conversion efficiencies.
    • Less need for maintenance because of more smooth operation of the plants

    There are a number of ways to handle the daily variations of the heat load. Two often used are large heat storages or using the district heating network as temporary storage. If it would be possible to centrally control the customer substations, it would also be possible to use heavy buildings connected to the district heating system as heat storages.

    To be able to find the best way to reduce or even eliminate the daily heat load variations, you need to understand the characteristics of the daily variations. This paper will describe a way of characterizing daily heat load variations in some Swedish district heating-systems.

    Download full text (pdf)
    FULLTEXT01
  • 6.
    Gadd, Henrik
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energiteknik.
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Daily heat load variations in Swedish district heating systems2013In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 106, p. 47-55Article in journal (Refereed)
    Abstract [en]

    Heat load variations in district heating systems are both seasonal and daily. Seasonal variations have mainly its origin from variations in outdoor temperature over the year. The origin of daily variations is mainly induced by social patterns due to customer social behaviours. Heat load variations cause increased costs because of increased peak heat load capacity and expensive peak fuels. Seasonal heat load variations are well-documented and analysed, but analyses of daily heat load variations are scarce. Published analyses are either case studies or models that try to predict daily heat load variations. There is a dearth of suitable assessment methods for more general analyses of existing daily load variations. In this paper, a novel assessment method for describing daily variations is presented. It is applied on district heating systems, but the method is generic and can be applied on every kind of activity where daily variations occur. The method was developed from two basic conditions: independent of system size and no use of external parameters other than of the time series analysed. The method consists of three parameters: the annual relative daily variation that is a benchmarking parameter between systems, the relative daily variation that describes the expected heat storage size to eliminate daily variations, and the relative hourly variation that describes the loading and unloading capacity to and from the heat storage. The assessment method could be used either for design purposes or for evaluation of existing storage. The method has been applied on 20 Swedish district heating systems ranging from small to large systems. The three parameters have been estimated for time series of hourly average heat loads for calendar years. The results show that the hourly heat load additions beyond the daily averages, vary between 3% and 6% of the annual volume of heat supplied to the network. Hereby, the daily variations are smaller than the seasonal variations, since the daily heat load additions, beyond the annual average heat load, are between 17% and 28% of the annual volume of heat supplied to the network. The size of short term heat storage to eliminate the daily heat load variations has been estimated to a heat volume corresponding to about 17% of the average daily heat supplied into the network. This conclusion can also be expressed as an average demand of 2.5 m3 of heat storage volume per TJ of heat supplied by assuming a water temperature difference of 40 C. The capacity for loading and unloading the storage should be equal to about half of the annual average heat load for heat supplied into the network. © 2013 Elsevier Ltd.

  • 7.
    Gadd, Henrik
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energiteknik.
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Heat load patterns in district heating substations2013In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 108, p. 176-183Article in journal (Refereed)
    Abstract [en]

    Future smart energy grids will require more information exchange between interfaces in the energy system. One interface where dearth of information exists is in district heating substations, being the interfaces between the distribution network and the customer building heating systems. Previously, manual meter readings were collected once or a few times a year. Today, automatic meter readings are available resulting in low cost hourly meter reading data. In a district heating system, errors and deviations in customer substations propagates through the network to the heat supply plants. In order to reduce future customer and heat supplier costs, a demand appears for smart functions identifying errors and deviations in the substations. Hereby, also a research demand appears for defining normal and abnormal heat load patterns in customer substations. The main purpose with this article is to perform an introductory analysis of several high resolution measurements in order to provide valuable information about substations for creating future applications in smart heat grids. One year of hourly heat meter readings from 141 substations in two district heating networks were analysed. The connected customer buildings were classified into five different customer categories and four typical heat load patterns were identified. Two descriptive parameters, annual relative daily variation and annual relative seasonal variation, were defined from each 1 year sequence for identifying normal and abnormal heat load patterns. The three major conclusions are associated both with the method used and the objects analysed. First, normal heat load patterns vary with applied control strategy, season, and customer category. Second, it is possible to identify obvious outliers compared to normal heat loads with the two descriptive parameters used in this initial analysis. Third, the developed method can probably be enhanced by redefining the customer categories by their indoor activities.

  • 8.
    Gadd, Henrik
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energy Science.
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Energy Science.
    Thermal energy storage systems for district heating and cooling2015In: Advances in Thermal Energy Storage Systems: Methods and Applications / [ed] Luisa F. Cabeza, Cambridge: Woodhead Publishing Limited, 2015, 1, p. 467-478Chapter in book (Refereed)
    Abstract [en]

    The context for this chapter is the current use and typical applications of thermal energy storages within contemporary district heating and cooling systems in the Nordic countries. Examples include a new assessment method, distributed heat storages, and hourly, daily, weekly, and seasonal heat and cold storages. Specific sizes have been estimated for 209 heat storages and 9 cold storages.

  • 9.
    Gadd, Henrik
    et al.
    Öresundskraft, Helsingborg, Sweden.
    Werner, Sven
    Halmstad University, School of Business, Innovation and Sustainability, The Rydberg Laboratory for Applied Sciences (RLAS).
    Thermal energy storage systems for district heating and cooling2021In: Advances in Thermal Energy Storage Systems: Methods and Applications / [ed] Luisa F. Cabeza, Duxford: Woodhead Publishing Limited, 2021, 2, p. 625-638Chapter in book (Refereed)
    Abstract [en]

    The context is the current use and typical applications of thermal energy storages within contemporary district heating and cooling systems. Storage examples and experiences are mostly provided from the Nordic countries in Europe. No focus is directed toward new storage methods or technical development of the current storage technologies used. Issues discussed are cash flows from storages, a variation assessment method, central versus distributed heat storages, hourly heat storage in networks, daily storages in both district heating and cooling systems, weekly heat storages, and seasonal heat storages. Recent investment costs are also summarized.

1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf