Accurate capacity planning is essential to ensure uninterrupted services and network stability through peak hours for the transport core network of KPN. This involves a trade-off between minimizing the risks of capacity shortages and costs of capacity expansions. High network loads are occurring more frequently and their magnitude is increasing. This necessitates measures to foresee high load situations before network capacity is surpassed. Currently, planning is based on manual predictions that lack substantiation. This research aims to improve network capacity planning by development of a forecast for the next year.
An analysis of the daily maximum traffic data of the transport core is performed, to determine the most suitable models for the prediction of network traffic. The data analysis, employing time series decomposition, revealed non-stationary trends and annual seasonality; traffic decreases throughout the summer and increases in the winter. An upward trend in the frequency and intensity of traffic peaks, highlights the growing demand and shifts in usage behavior. The extreme traffic peaks in the historical data were correlated to F1 race days and other anticipated events.
Two algorithms that integrate exogenous variables were assessed to predict the extreme values. The models either yielded inaccurate traffic predictions or encountered challenges in interpretability and pattern recognition, with the limited amount of data available. In response to these limitations, a decomposed forecast was created that predicts the trend and seasonality. Furthermore, Extreme Value Analysis (EVA) was implemented to address the extreme values in the data.
The final prediction framework combines the decomposed forecast with EVA for the next six quarters and outperforms the other models. The model effectively captures extreme values and provides insights into the maximum expected peaks and risk levels. The substantiated forecasts of the EVA model and the manual predictions yielded comparable results. However, the EVA model provides better insights into the likelihood of exceeding specific traffic values, which enhances capacity calculations and precision.
The prediction framework has been integrated into the business interface of KPN, which marks the initial step in the automatization of short-term capacity planning. The research insights emphasize the intricate nature of accurate prediction of future demand and advocate for scalable solutions beyond building new capacity. These solutions range from short-term mitigation to long-term strategies designed to alleviate high network loads. They underscore the importance of the implementation and integration of
dynamic decision-making within a digital twin of the network to ensure sustained effectiveness.

This report presents a bed exit prediction model using a decision tree classifier, a machine learning method. This system is designed to alarm a caregiver in a nursing home to prevent bed­ falls among institutionalized elderly. For the input of this algorithm, features are derived from four force sensing resistor sensors and six piezoelectric sensors mounted on a device which is placed under the mattress at chest level. The classifier is based on data containing 98 bed exits of two different people on two different mattresses. The best results were obtained for the feature which uses the standard deviation of the force sensing resistor sensors of the last 10 seconds. The classifier was evaluated using a test set which contained a total of 645 time frames of 5 seconds of 23 cases where someone leaves the bed and 622 time frames where there is no bed exit. 10 windows with bed exits were classified correctly out of 23. Moreover, the system has a false alarm rate of 3.9%, meaning that in 24 time windows a bed exit is predicted while there is no actual bed exit. The accuracy with which the model classifies a bed exit or no bed exit correctly 5 seconds beforehand is 94.3%. Thereafter, it was researched whether adding an extra sensor plate would be helpful to improve the bed exit prediction. The extra plate should be placed on the side of the bed if the preferred side for getting up is known, or at the hip level if the side is unknown. This is of added value to define the bed status of the client as this plate, in contradiction to the sensor plate at chest level, still measures pressure when someone sits up straight.