  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.
