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Article

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

1
DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany
2
Elenia Institute for High Voltage Technology and Power Systems, Technische Universität Braunschweig, Schleinitzstraße 23, 38106 Braunschweig, Germany
3
Hammer Real GmbH, Sylvensteinstr. 2, 81369 Munich, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Energies 2021, 14(12), 3576; https://doi.org/10.3390/en14123576
Received: 28 April 2021 / Revised: 8 June 2021 / Accepted: 14 June 2021 / Published: 16 June 2021
(This article belongs to the Collection Demand Management for Buildings and Industrial Facilities)
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. View Full-Text
Keywords: time-series prediction; machine learning; LSTM; personalized standard load profiles; load management; battery electric vehicles; charging strategies time-series prediction; machine learning; LSTM; personalized standard load profiles; load management; battery electric vehicles; charging strategies
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MDPI and ACS Style

Steens, T.; Telle, J.-S.; Hanke, B.; von Maydell, K.; Agert, C.; Di Modica, G.-L.; Engel, B.; Grottke, M. A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV. Energies 2021, 14, 3576. https://doi.org/10.3390/en14123576

AMA Style

Steens T, Telle J-S, Hanke B, von Maydell K, Agert C, Di Modica G-L, Engel B, Grottke M. A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV. Energies. 2021; 14(12):3576. https://doi.org/10.3390/en14123576

Chicago/Turabian Style

Steens, Thomas, Jan-Simon Telle, Benedikt Hanke, Karsten von Maydell, Carsten Agert, Gian-Luca Di Modica, Bernd Engel, and Matthias Grottke. 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV" Energies 14, no. 12: 3576. https://doi.org/10.3390/en14123576

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