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

Steens, Thomas and Telle, Jan-Simon and Hanke, Benedikt and Maydell, Karsten von and Agert, Carsten and Di Modica, Gian-Luca and Engel, Bernd and Grottke, Matthias (2021) A Forecast-Based Load Management Approach for CommercialBuildings Demonstrated on an Integration of BEV. Energies, pp. 1-25. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en14123576. ISSN 1996-1073.

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Official URL: https://www.mdpi.com/1996-1073/14/12/3576

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.

Item URL in elib:https://elib.dlr.de/142793/
Document Type:Article
Title:A Forecast-Based Load Management Approach for CommercialBuildings Demonstrated on an Integration of BEV
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Steens, ThomasThomas.Steens (at) dlr.dehttps://orcid.org/0000-0002-4218-3015
Telle, Jan-SimonJan-Simon.Telle (at) dlr.dehttps://orcid.org/0000-0001-6228-6815
Hanke, Benediktbenedikt.hanke (at) dlr.dehttps://orcid.org/0000-0001-7927-0123
Maydell, Karsten vonKarsten.Maydell (at) dlr.dehttps://orcid.org/0000-0003-0966-5810
Agert, Carstencarsten.agert (at) dlr.dehttps://orcid.org/0000-0003-4733-5257
Di Modica, Gian-LucaTechnische Universität Braunschweig, elenia Institute for High Voltage Technology and Power SystemsUNSPECIFIED
Engel, BerndTechnische Universität Braunschweig, elenia Institute for High Voltage Technology and Power SystemsUNSPECIFIED
Grottke, MatthiasHammer Real GmbHUNSPECIFIED
Date:16 June 2021
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.3390/en14123576
Page Range:pp. 1-25
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Demand Management for Buildings and Industrial Facilities
ISSN:1996-1073
Status:Published
Keywords:time-series prediction; machine learning; LSTM; personalized standard load profiles;load management; battery electric vehicles; charging strategies
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Digitalization and System Technology
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Energy System Technology
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Steens, Thomas
Deposited On:21 Jun 2021 11:20
Last Modified:12 Jul 2021 10:10

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