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Technologie- und Klimawandel : Energie – Gebäude – Umwelt
E-Nova 2020(24) = Science.Research.Pannonia. 22

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    Publication
    Using FusiX platform for Intelligent Energy Management Systems’ development
    (Leykam, 2020-11-26)
    Georgoutsos, V. 
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    Soulioti, G. 
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    Alifragkis, V. 
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    Livanos, N. 
    A novel Decision Support System (DSS) development framework, named FusiX, with integrated simulation support, Graphical User Interface (GUI) support and data fusion engine, is developed to meet the needs of the modern building energy management sector. Its main objective is to facilitate and streamline the development and the expansion of a complete DSS. FusiX constitutes a versatile base on which Intelligent Energy Management Systems (IEMS) can be built, allowing a system engineer to incorporate different data resources into a single intelligent system. Its data fusion engine can process heterogeneous data from historic, real time sensed data, simulated and predicted data independently from their location (local or remote). The software system is extended with a web-based GUI for efficient administration, exposing all real-time measurements and available commands to authorized users, supporting user alerting, as well as providing means to produce and export business and technical reports.
      134  127
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    Publication
    An Integrated Framework for Building’s Energy Management based on Deep Learning
    (Leykam, 2020-11-26)
    Meintanis, I. 
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    Monios, N. 
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    Livieris, I.E. 
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    Kampourakis, M. 
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    Fourakis, S. 
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    Kyriakoulis, N. 
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    Kokkorikos, S. 
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    Chondronasios, A. 
    Forecasting the building energy consumption constitutes a significant factor for a wide variety of applications including planning, management and optimization. Nowadays, research is focused towards the development of more efficient and sustainable energy management systems which focus on minimising energy waste. These systems are based on intelligent models, which provide accurate predictions of future energy demand/load, both at aggregate and individual site level. In this work, we present a holistic integrated solution for the buildings’ energy management systems using deep learning methods. The proposed solution is based on efficient deep-learning forecasting models for short-term local weather parameters and energy load consumption. The developed forecasting models are integrated into the smart energy management system of the building for taking the proper decisions to ensure efficient utilization of energy resources.
      160  112
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    Untersuchung eines Elektroabscheiderkonzepts zur Reduktion von Staubemissionen
    (Leykam, 2020-11-26)
    Schittl, Florian 
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    Jauschnik, Gabriela 
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    Pöttler, Martin 
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    Krail, Jürgen 
    Particulate emissions are formed during the combustion of biogenic fuels depending on the type of furnace, the operating conditions in terms of the combustion quality and the different fuel properties. The release of especially small particles often leads to health problems such as the development or worsening of lung diseases. Downstream electrostatic precipitators (ESP) represent a state of the art separation technology in medium and large biomass plants. However, these precipitators are often difficult to implement in smaller furnaces due to economic aspects and space constraints. This study deals with the integration and experimental investigation of an ESP system into the boiler body of a small scaled biomass furnace (< 100 kW). In Addition to the full load behaviour of the firing system, further test arrangements with different part load conditions of the boiler are being considered in order to analyse the particle precipitation under realistic plant operation with regard to flue gas properties and flow conditions. Furthermore, different fuels are considered. Both, discontinuous as well as time-resolved aerosol measuring methods are used to determine particulate matter emissions. The results of the discontinuous dust measurements show that with the integrated ESP, at least 50 % of the particles in the fine dust range are separated, both at full and partial load operation of the boiler, irrespective of the fuel used. Furthermore, it is shown that partial load conditions favour the separation efficiency due to low velocities and low temperatures of the gas flow over the discharge electrode, which is situated in the reversing chamber. Accordingly, the separation efficiency in part load is between 65 and 85 %, depending on fuel used. In order to enable a more precise observation of the separation behaviour with regard to particle size, additional continuous ELPI (electrical low pressure impactor) measurements are carried out for a selected fuel (wood chips). These measurements show that for small particle collectives (dP < 1 μm) separation efficiencies of over 55 % (full load) and over 80 % (part load) are achieved.
      5718  210
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    Publication
    Complex glass facade modelling for Model Predictive Control of thermal loads: impact of the solar load identification on the state-space model accuracy
    (Leykam, 2020-11-26)
    Veynandt, Francois 
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    Heschl, Christian 
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    Klanatsky, Peter 
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    Plank, Helmut 
    Above and beyond improving the efficiency of the building envelope and the energy supply system, the demand-side flexibility in terms of load shifting and peak reduction are vital factors in further increasing the share of volatile renewable energy sources. The thermal activation of building components, like floors and ceilings, enables the cost-effective potential for short-term energy storage to fulfil these requirements. In order to exploit the storage capabilities of active building systems, a reliable model predicted control (MPC) approach is required. However, primarily if a large glass façade element is utilised, the appropriate modelling of solar loads is critical for an effective MPC operation. Hence, based on a dynamic building simulation tool, a characteristic map for the solar load prediction of a glass façade system in combination of external venetian blinds was generated to enhance the state-space model approach for the MPC algorithm. The comparison with a conventional state-space model approach shows the integration of a detailed characteristic map can only marginally improve the prediction accuracy. The additional information required from the glass façade manufacturer and the associated simulation effort is not of substantial value. In contrast, the conventional grey box model enables an entirely datadriven parameter identification, without the manufacturers’ data. Furthermore, the MPC optimisation procedure, searching for the best control strategy, can be more efficient (solver-based optimisation), with shorter computing turnaround times.
      171  134
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