Day-ahead industrial load forecasting for electric RTG cranes

Feras Alasali, Stephen Haben, Victor Becerra, William Holderbaum

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)223-234
JournalJournal of Modern Power Systems and Clean Energy
Issue number2
Early online date27 Feb 2018
Publication statusPublished - 1 Mar 2018

Bibliographical note

© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


Dive into the research topics of 'Day-ahead industrial load forecasting for electric RTG cranes'. Together they form a unique fingerprint.

Cite this