Abstract
Given the increase in international trading and the significant energy and environmental challenges in ports around the world, there is a need for a greater understanding of the energy demand behaviour at ports. The move towards electrified rubber-tyred gantry (RTG) cranes is expected to reduce gas emissions and increase energy savings compared to diesel RTG cranes but it will increase electrical energy demand. Electrical load forecasting is a key tool for understanding the energy demand which is usually applied to data with strong regularities and seasonal patterns. However, the highly volatile and stochastic behaviour of the RTG crane demand creates a substantial prediction challenge. This paper is one of the first extensive investigations into short term load forecasts for electrified RTG crane demand. Options for model inputs are investigated depending on extensive data and correlation analysis. The effect of estimation accuracy of exogenous variables on the forecast accuracy is investigated as well. The models are tested on two different RTG crane data sets that were collected from the Port of Felixstowe in the UK. The results reveal the effectiveness of the forecast models when the estimation of the number of crane moves and container gross weight are accurate.
Highlights
Over the last decade, the amount of international trading worldwide has increased rapidly and ports are facing significant environmental and energy challenges such as rising fossil fuels prices and greenhouse emissions
While there is a large quantity of load forecasting research, the rubber-tyred gantry (RTG) crane demand forecasting literature is more limited and complex compared to typical distribution loads
There are a number of challenges facing load forecasting of RTG cranes
Summary
The amount of international trading worldwide has increased rapidly and ports are facing significant environmental and energy challenges such as rising fossil fuels prices and greenhouse emissions. Shifting from diesel to electrified RTG cranes will lead to an increase in peak demands and power consumption at the port substations. In this situation, ports may need to upgrade the electrical infrastructure to meet this rise in demand [3]. There is a gap and lack of understanding of the ports and the RTG crane energy demand behaviour This understanding is vital for developing power generation strategies to reduce the environmental effects of gas emissions and peak demand problems [4]. A large variety of methodologies and models have been employed in order to achieve an accurate short-term load forecast. These models are mainly divided into three categories: 1) Traditional or statistical methods: for example, autoregressive integrated moving average with exogenous variable (ARIMAX) and autoregressive with exogenous variable (ARX) [8]
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