The prediction task is attracting more and more attention among the power system community. Accurate predictions of electrical quantities up to a few hours ahead (e.g. renewable production, electrical load etc.) are for instance crucial for distribution system operators to operate their network in the presence of a high share of renewables, or for energy producers to maximise their profits by optimising their portfolio management. In the literature, statistical approaches are usually proposed to predict electrical quantities. In the present study, the authors present a novel method based on matrix factorisation. The authors' approach is inspired by the literature on data mining and knowledge discovery and the methodologies involved in recommender systems. The idea is to transpose the problem of predicting ratings in a recommender system to a problem of forecasting electrical quantities in a power system. Preliminary results on a real wind speed dataset tend to show that the matrix factorisation model provides similar results than auto regressive integrated models in terms of accuracy (MAE and RMSE). The authors' approach is nevertheless highly scalable and can deal with noisy data (e.g. missing data).

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Published on 01/01/2017

DOI: 10.1049/opa-cired.2017.1229
Licence: CC BY-NC-SA license

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