Day-ahead industrial load forecasting for electric RTG cranes

• Main Authors: Feras ALASALI, Stephen HABEN, Victor BECERRA, William HOLDERBAUM
• Format: Article
• Language: English
• Published: IEEE 2018-02-01
• Series: Journal of Modern Power Systems and Clean Energy
• Subjects: Rubber-tyred gantry (RTG) cranes; Correlation analysis; Exogenous variables estimation; Artificial neural networks; Time series forecast modelling
• Online Access: http://link.springer.com/article/10.1007/s40565-018-0394-4
• ISSN: 2196-5625; 2196-5420

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.