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  • 1.
    Ding, Yiyu
    et al.
    NTNU, Norway.
    Timoudas, Thomas Ohlson
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Wang, Qian
    KTH Royal Institute of Technology, Sweden; Uponor AB,Sweden.
    Chen, Shuqin
    Zhejiang University, China.
    Brattebø, Helge
    NTNU, Norway.
    Nord, Natasa
    NTNU, Norway.
    A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries2022Inngår i: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 269, artikkel-id 116163Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the face of green energy initiatives and progressively increasing shares of more energy-efficient buildings, there is a pressing need to transform district heating towards low-temperature district heating. The substantially lowered supply temperature of low-temperature district heating broadens the opportunities and challenges to integrate distributed renewable energy, which requires enhancement on intelligent heating load prediction. Meanwhile, to fulfill the temperature requirements for domestic hot water and space heating, separate energy conversion units on user-side, such as building-sized boosting heat pumps shall be implemented to upgrade the temperature level of the low-temperature district heating network. This study conducted hybrid heating load prediction methods with long-term and short-term prediction, and the main work consisted of four steps: (1) acquisition and processing of district heating data of 20 district heating supplied nursing homes in the Nordic climate (2016–2019); (2) long-term district heating load prediction through linear regression, energy signature curve in hourly resolution, providing an overall view and boundary conditions for the unit sizing; (3) short-term district heating load prediction through two Artificial Neural Network models, f72 and g120, with different prediction input parameters; (4) evaluation of the predicted load profiles based on the measured data. Although the three prediction models met the quality criteria, it was found that including the historical hourly heating loads as the input to the forecasting model enhanced the prediction quality, especially for the peak load and low-mild heating season. Furthermore, a possible application of the heating load profiles was proposed by integrating two building-sized heat pumps in low-temperature district heating, which may be a promising heat supply method in low-temperature district heating. © 2022 The Authors

  • 2.
    Roy, Arghyamalya
    et al.
    Linköping University, Sweden.
    Bersellini Farinotti, Alex
    Karolinska Institute, Sweden.
    Arbring Sjöström, Theresia
    Linköping University, Sweden.
    Abrahamsson, Tobias
    Linköping University, Sweden.
    Cherian, Dennis
    Linköping University, Sweden.
    Karaday, Michal
    Palacký University, Czech Republic.
    Tybrandt, Klas
    Linköping University, Sweden.
    Nilsson, David
    RISE Research Institutes of Sweden, Digitala system, Smart hårdvara.
    Berggren, Magnus
    Linköping University, Sweden.
    Poxson, David
    Linköping University, Sweden.
    Svensson, Camilla
    Karolinska Institute, Sweden.
    Simon, Daniel
    Linköping University, Sweden.
    Electrophoretic Delivery of Clinically Approved Anesthetic Drug for Chronic Pain Therapy2023Inngår i: Advanced Therapeutics, E-ISSN 2366-3987, Vol. 6, nr 7, artikkel-id 2300083Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Despite a range of available pain therapies, most patients report so-called “breakthrough pain.” Coupled with global issues like opioid abuse, there is a clear need for advanced therapies and technologies for safe and effective pain management. Here the authors demonstrate a candidate for such an advanced therapy: precise and fluid-flow-free electrophoretic delivery via organic electronic ion pumps (OEIPs) of the commonly used anesthetic drug bupivacaine. Bupivacaine is delivered to dorsal root ganglion (DRG) neurons in vitro. DRG neurons are a good proxy for pain studies as they are responsible for relaying ascending sensory signals from nociceptors (pain receptors) in the peripheral nervous system to the central nervous system. Capillary based OEIPs are used due to their probe-like and free-standing form factor, ideal for interfacing with cells. By delivering bupivacaine with the OEIP and recording dose versus response (Ca2+ imaging), it is observed that only cells close to the OEIP outlet (≤75 µm) are affected (“anaesthetized”) and at concentrations up to 10s of thousands of times lower than with bulk/bolus delivery. These results demonstrate the first effective OEIP deliveryof a clinically approved and widely used analgesic pharmaceutical, and thus are a major translational milestone for this technology. © 2023 The Authors.

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