The large-scale pervasion of wind generators in electric power systems has raised the requirement for fast and reliable forecasting algorithms, aimed at allowing system operators to effectively manage the intrinsic uncertainties induced by their non-programmable generation profiles. To face with the dichotomy between forecasting accuracy and efficiency, this paper explores the potential role of Principal Component Analysis in training a neural-network forecaster, by extracting actionable intelligence from large data sets of historical climatic variables. The results obtained on a real case study are presented and discussed in order to assess the benefits from the application of the proposed method.

The role of principal component analysis in neural-based wind power forecasting

De Caro, F.
;
Vaccaro, A.
;
Villacci, D.
2018-01-01

Abstract

The large-scale pervasion of wind generators in electric power systems has raised the requirement for fast and reliable forecasting algorithms, aimed at allowing system operators to effectively manage the intrinsic uncertainties induced by their non-programmable generation profiles. To face with the dichotomy between forecasting accuracy and efficiency, this paper explores the potential role of Principal Component Analysis in training a neural-network forecaster, by extracting actionable intelligence from large data sets of historical climatic variables. The results obtained on a real case study are presented and discussed in order to assess the benefits from the application of the proposed method.
2018
9781538619537
Big Data; Knowledge Discovery; Machine Learning; Principal Component Analysis; Wind Power Forecasting; Computer Networks and Communications; Electrical and Electronic Engineering; Energy Engineering and Power Technology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/37554
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