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A Forecast Based Load Management Approach For Commercial Buildings -- Comparing LSTM And Standardized Load Profile Techniques
arXiv - CS - Systems and Control Pub Date : 2020-07-14 , DOI: arxiv-2007.06832 Thomas Steens, Jan-Simon Telle, Benedikt Hanke, Karsten von Maydell, Carsten Agert, Gian-Luca di Modica, Bernd Engel, Matthias Grottke
arXiv - CS - Systems and Control Pub Date : 2020-07-14 , DOI: arxiv-2007.06832 Thomas Steens, Jan-Simon Telle, Benedikt Hanke, Karsten von Maydell, Carsten Agert, Gian-Luca di Modica, Bernd Engel, Matthias Grottke
Load-forecasting problems have already been widely addressed with different
approaches, granularities and objectives. Recent studies focus 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 the behaviour of two
neural networks, Long Short-Term Memory and Feed Forward Neural Network and two
statistical methods, standardized load profiles and personalized standardized
load profiles are analysed and assessed by using a sliding-window forecast
approach. The results show that machine learning algorithms have the benefit of
being able to adapt to new patterns, whereas the personalized standardized load
profile performs similar to the tested deep learning algorithms on the metrics.
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 shows that such a system can lead to significantly lower load
peaks, exceeding a defined grid limit, and to a lower number of overloads
compared to uncontrolled charging.
中文翻译:
基于预测的商业建筑负载管理方法——比较 LSTM 和标准化负载曲线技术
负载预测问题已经通过不同的方法、粒度和目标得到广泛解决。最近的研究不仅侧重于深度学习方法,还侧重于预测单个建筑物级别的负载。本研究旨在研究通过使用不同的负载预测技术来管理负载而出现的问题和可能性。为此,使用滑动窗口预测方法分析和评估了两个神经网络,长短期记忆和前馈神经网络以及两种统计方法,标准化负载曲线和个性化标准化负载曲线的行为。结果表明,机器学习算法具有能够适应新模式的优势,而个性化标准化负载配置文件在指标上的表现与经过测试的深度学习算法相似。作为评估负载预测对能源管理系统应用支持的案例研究,通过使用负载预测来安排充电程序来模拟充电站与现有建筑物的集成。它表明,与不受控制的充电相比,这样的系统可以显着降低负载峰值,超过定义的电网限制,并减少过载次数。
更新日期:2020-07-15
中文翻译:
基于预测的商业建筑负载管理方法——比较 LSTM 和标准化负载曲线技术
负载预测问题已经通过不同的方法、粒度和目标得到广泛解决。最近的研究不仅侧重于深度学习方法,还侧重于预测单个建筑物级别的负载。本研究旨在研究通过使用不同的负载预测技术来管理负载而出现的问题和可能性。为此,使用滑动窗口预测方法分析和评估了两个神经网络,长短期记忆和前馈神经网络以及两种统计方法,标准化负载曲线和个性化标准化负载曲线的行为。结果表明,机器学习算法具有能够适应新模式的优势,而个性化标准化负载配置文件在指标上的表现与经过测试的深度学习算法相似。作为评估负载预测对能源管理系统应用支持的案例研究,通过使用负载预测来安排充电程序来模拟充电站与现有建筑物的集成。它表明,与不受控制的充电相比,这样的系统可以显着降低负载峰值,超过定义的电网限制,并减少过载次数。