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A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV

Author

Listed:
  • Thomas Steens

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Jan-Simon Telle

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Benedikt Hanke

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Karsten von Maydell

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Carsten Agert

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Gian-Luca Di Modica

    (Elenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Schleinitzstraße 23, 38106 Braunschweig, Germany)

  • Bernd Engel

    (Elenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Schleinitzstraße 23, 38106 Braunschweig, Germany)

  • Matthias Grottke

    (Hammer Real GmbH, Sylvensteinstr. 2, 81369 Munich, Germany)

Abstract

Load-forecasting problems have already been widely addressed with different approaches, granularities and objectives. Recent studies focused not only on deep learning methods but also on forecasting loads on single building level. This study aims to research problems and possibilities arising by using different load-forecasting techniques to manage loads. For that behavior of two neural networks, Long Short-Term Memory and Feed-Forward Neural Network as well as two statistical methods, standardized load profiles and personalized standardized load profiles are analyzed and assessed by using a sliding-window forecast approach. The results show that personalized standardized load profiles (MAE: 3.99) can perform similar to deep learning methods (for example, LSTM MAE: 4.47). However, because of the simplistic approach, load profiles are not able to adapt to new patterns. As a case study for evaluating the support of load-forecasting for applications in energy management systems, the integration of charging stations into an existing building is simulated by using load-forecasts to schedule the charging procedures. It is shown that forecast- based controlled charging can have a significant impact by lowering overload peaks exceeding the house connection point power limit (controlled charging 20.24 kW; uncontrolled charging: 65.15 kW) while slightly increasing average charging duration. It is concluded that integration of high flexible loads can be supported by using forecast-based energy management systems with regards to their limitations.

Suggested Citation

  • Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3576-:d:575802
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    References listed on IDEAS

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