Time Series Machine Learning Technique with Application to Barcelona Metro Station Energy Minimization
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This thesis is part of the SEAM4US project, which goal is to minimize the energy consumption of the Barcelona metro station. The energy minimization is done by so-called model predictive control, i.e. management of the energy using systems based on a time series prediction. Here we focus on such a prediction. We make use of the popular Fourier transformation in combination with trend detection and validate our method by testing on different data sets and comparing with well-known techniques. Furthermore we conclude that this technique is very usable for the SEAM4US project and probably for a lot of time series prediction.