Analysis of RTG crane load demand and short-term load forecasting

The increasing numbers of international trading ports around the world are facing significant energy and environmental challenges such as rising energy consumption and greenhouse emissions. To understand the energy demand behaviour of ports or cranes, several simulation studies have been carried out using data from the Port of Felixstowe in the UK. The aim of this paper is to propose a 24-hours active power forecast model and analysis tools for a single electrified RTG crane. This model could be a potential solution to these energy consumption and management problems. The crane data has been collected for 30 days and analysed in terms of the daily demand usage, the number of crane moves and the weight of containers. Two different forecast methods, ARIMAX and Artificial Neural Network have been used to forecast highly stochastic, non-smooth and very volatile active crane power demand. The results indicate that the ANN forecast model is more accurate according to the mean absolute percentage error (MAPE) results during the testing period.