基于预测的商业建筑负荷管理方法--LSTM与标准化负荷剖面技术的比较
A Forecast Based Load Management Approach For Commercial Buildings -- Comparing LSTM And Standardized Load Profile Techniques
摘要：
负荷预测问题已经得到了广泛的解决与不同的方法，粒度和目标。最近的研究不仅关注深度学习方法，还关注单个建筑物水平的负荷预测。本研究旨在探讨使用不同的负荷预测技术来管理负荷所产生的问题和可能性。为此，两个神经网络的行为，长短期记忆和前馈神经网络和两种统计方法，标准化负荷曲线和个性化的标准化负荷曲线进行了分析和评估，通过使用滑动窗口预测方法。结果表明，机器学习算法具有能够适应新模式的优势，而个性化的标准化负载配置文件在指标上与测试的深度学习算法相似。作为评估能源管理系统应用程序负载预测支持的案例研究，通过使用负载预测来调度充电程序，模拟充电站与现有建筑物的集成。它表明，这样的系统可以导致显著较低的负荷峰值，超过规定的电网限制，并减少过载的数量相比，不受控制的充电。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
