Short-Term Load Forecasting Methods for Maritime Container Terminals

Procurement of electricity gains more and more focus in enterprises especially with the introduction of electric mobility. At a maritime container terminal the electricity consumption is highly related to the number of container movements of each day. Short-term load forecasting (STLF) methods have not yet been systematically researched when applied to container terminals. Therefore it seems reasonable that the inclusion of knowledge about the number of next day’s container movements might improve the forecasting of next day’s electricity demand of the container terminal. One way to include this knowledge in the forecasting process is to use Case-Based Reasoning methods. In this thesis a concept for a corresponding system is outlined and implemented. In addition, also further concepts for using established STLF-methods are described and implemented. Besides the system based on Case-Based Reasoning, naive methods, time-series models, artificial neural networks and simulation are being implemented and compared to each other. The implementations are tested with data from the use-case ContainerTerminal Altenwerder in Hamburg, Germany. Goal of the thesis is to evaluate, which method is best suited in what situation.