In this thesis, we use data science / statistical techniques to better understand the energy consumption behind a powder drying facility located in Zwolle, as part of Abbott's initiative to better manage its energy consumption. As powder drying is by far the facility's most energy intensive process, this project therefore focuses exclusively on powder drying.
The primary goal is to develop an accurate predictive model for the energy consumption, which can be used as a baseline to assess whenever or wherever the largest changes in energy efficiency took place. This can in turn be used to influence the energy policy, and may for example be used to assess what kind of retrofits can have the largest positive effects on efficiency.
Hereby we consider a variety of predictive models in increasing order of complexity. We also make our own contribution by describing a predictive model based on cluster-analysis, where we fit separate models on each cluster in order to better capture their specific patterns. It turns out that the latter approach is capable of drastically improving predictive performance.

The primary goal of this report is to provide a general overview of offline change-point literature as it is known today. Change-point methods are important statistical problems, where we are interested in determining whenever a certain data-set changes in structure. Furthermore, the term "off-line" is meant to indicate that the data itself is already known, whereas on the other hand we have "on-line" methods which deal with situations where new data is yet being received during localisation of the change-points. In this report we mainly consider off-line methods, as we feel off-line methods provide a more friendly introduction into change-point analysis and on-line methods are in principle just an extension of their off-line counterparts. First off, these off-line change-point methods are considered under different assumptions (parametric, non-parametric). In each case, we treat a solution to the change-point problem under different models(normal and gamma model, mean or varianche change etc.). Eventually we shall also treat some widely used algorithms, meant to extend the problem into the localisation of multiple change-points.indent Aside from theoretical considerations, an equally important part of this report will be focused on empirical results. Both for the statistics as algorithms will there be a performance study where the different methods will be empirically assessed and compared under different models, namely the robustness against "outliers"(extreme data-values) will be investigated. So to complement the primary goal, we will also focus on the following two subgoals:
1) Assessment and comparison of different change-point models
2) Evaluating and improving robustness against outliers