Characteristics of day-ahead wind power forecast errors in Nordic countries and benefits of aggregation

Jari Miettinen (Corresponding Author), Hannele Holttinen

    Research output: Contribution to journalArticleScientificpeer-review

    10 Citations (Scopus)

    Abstract

    The growing proportion of wind power in the Nordic power system increases day-ahead forecasting errors, which have a link to the rising need for balancing power. However, having a large interconnected synchronous power system has its benefits, because it enables to aggregate imbalances from large geographical areas. In this paper, day-ahead forecast errors from four Nordic countries and the impacts of wind power plant dispersion on forecast errors in areas of different sizes are studied. The forecast accuracy in different regions depends on the amount of the total wind power capacity in the region, how dispersed the capacity is and the forecast model applied. Further, there is a saturation effect involved, after which the reduction in the relative forecast error is not very large anymore. The correlations of day-ahead forecast errors between areas decline rapidly when the distance increases. All error statistics show a strong decreasing trend up to the area sizes of 50,000 km2. The average mean absolute error (MAE) in different regions is 5.7% of installed capacity. However, MAE of a smaller area can be over 8% of the capacity, but when all the Nordic regions are aggregated together, the capacity-normalized MAE decreases to 2.5%. The average of the largest errors for different regions is 39.8% and when looking at the largest forecast errors for smaller areas, the largest errors can exceed 80% of the installed capacity, whereas at the Nordic level, the maximum forecast error is only 13.5% of the installed capacity.
    Original languageEnglish
    Pages (from-to)959-972
    Number of pages14
    JournalWind Energy
    Volume20
    Issue number6
    DOIs
    Publication statusPublished - 2017
    MoE publication typeA1 Journal article-refereed

    Keywords

    • wind power forecast errors
    • wind power forecasting
    • forecast error smoothing

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