Introducing the Temporal Distortion Index to perform a bidimensional analysis of renewable energy forecast. (1st January 2016)

• Record Type: Journal Article
• Authors: Frías-Paredes, Laura; Mallor, Fermín; León, Teresa; Gastón-Romeo, Martín
• Is Part Of: Energy. Volume 94(2016)
• Page Start: 180
• Page End: 194
• Publication Date: 2016-01-01
• Subjects: Forecast accuracy -- Temporal misalignment -- Dynamic time warping -- Renewable energy -- Temporal distortion index -- Bidimensional error; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.10.093
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic Time Warping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verification measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case. Highlights: The paper deals with the assessment of timing errors in renewable energy forecast. The new Temporal Distortion Index is defined and its properties analyzed. A set of synthetic and real examples show the usefulness of the new index. Bi-dimensional analysis of forecast errors using temporal and scale deviations. The methodology is useful to compare renewable energy prediction models.