PVadapt

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Principal Investigator
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Start Date
October 1, 2018
End Date
March 31, 2022
Description
Prefabrication, Recyclability and Modularity for cost reductions in Smart BIPV systems

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Now showing 1 - 3 of 3
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    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.
      175  114
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    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. 
    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.
      193  108
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    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)
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    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.
      210  155