e-space
Manchester Metropolitan University's Research Repository

Day-ahead industrial load forecasting for electric RTG cranes

Alasali, F and Haben, S and Becerra, V and Holderbaum, W (2018) Day-ahead industrial load forecasting for electric RTG cranes. Journal of Modern Power Systems and Clean Energy. ISSN 2196-5625

[img]
Preview

Available under License Creative Commons Attribution.

Download (1MB) | Preview

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.

Impact and Reach

Statistics

Downloads
Activity Overview
28Downloads
78Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

Edit Item Edit Item