Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors
Abstract Recent years have seen a growing trend in wind and solar energy generation globally and it is expected that an important percentage of total energy production comes from these energy sources. However, they present inherent variability that implies fluctuations in energy generation that are difficult to forecast. Thus, forecasting errors have a considerable role in the impacts and costs of renewable energy integration, management, and commercialization. This study presents an important advance in the task of analyzing prediction models, in particular, in the timing component of prediction error, which improves previous pioneering results. A new method to match time series is defined…
Dynamic mean absolute error as new measure for assessing forecasting errors
Abstract Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the forecast error. The trade-off between both types of errors is computed, analyzed, and interpreted. Moreover, a new index, the dynamic mean absolute error, DMAE, is defined to measure the prediction accuracy. This index accounts for both error components: temporal and absolute. Real cases of wind …
Introducing the Temporal Distortion Index to perform a bidimensional analysis of renewable energy forecast
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…
A Morphological Clustering Method for daily solar radiation curves
Abstract We present a new method based on Mathematical Morphology techniques for the classification of solar radiation curves that we call MfCM. The main advantage of using MfCM as opposed to daily clearness index distributions is that it allows us to keep the dynamics of the solar radiance curves in the analysis: both cloud transitions and variability in direct radiation are simultaneously taken into account. To illustrate our proposal, we use a set of real radiation data collected in a location sited in southern Spain.