Day-ahead industrial load forecasting for electric RTG cranes

Feras Alasali, Stephen Haben, Victor Becerra, William Holderbaum

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    Abstract

    Given the increase in international trading and the signficant 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.
    Original languageEnglish
    Number of pages12
    JournalJournal of Modern Power Systems and Clean Energy
    Early online date27 Feb 2018
    DOIs
    Publication statusEarly online - 27 Feb 2018

    Keywords

    • Rubber-tyred gantry (RTG) cranes
    • Correlation analysis
    • Exogenous variables estimation
    • Artificial neural networks
    • Time series forecast modelling

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