Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors

Abstract

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.

Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO

“…where, r p is the Pearson's correlation coefficient, ∑ d x d y is the product sum of squares, ∑ d 2 x ∑ d 2 y are the sum of squares of X and Y respectively, and r s is the Spearman's correlation coefficient, d i = X i − Y i is the variation in the rankings of the corresponding variables, and N is the number of observations. The results of the models were evaluated under strict statistical metrics whose mathematical formulations were found and adapted from the literature [56,57]. The statistical methods used to assess the efficiency of the models were the root mean square error (RMSE), the root mean square percentage error (RMSPE), the mean absolute error (MAE), and the mean absolute error in percent (MAPE), where Equations ( 3)-( 6) were used, respectively.…”

High Penetration of Renewable Energy Sources and Power Market Formation for Countries in Energy Transition: Assessment via Price Analysis and Energy Forecasting

“…An alternative approach to determine the appropriate model is to perform a grid search, i.e., generating and testing all combinations of parameters within certain bounds and evaluating the model results according to the given criteria-for example, Akaike information criterion (AIC) [31], Bayesian information criterion (BIC) [32], Hannan-Quinn information criterion (HQIC) [33] as well as the frequently-used metrics [34] as mean absolute percentage error (MAPE) and root mean square error (RMSE). The ranking of the best-performing models, estimated by a grid search within the following ranges-p = 0 ÷ 5; d = 0 ÷ 2; q = 0 ÷ 5; P = 0 ÷ 3; D = 0 ÷ 2; Q = 0 ÷ 3-is shown in Table 7 for the solar data and Table 8 for the wind data.…”

A workflow for including atmospheric stability effects in wind resource and yield assessment and its evaluation against wind measurements and SCADA

“…Statistical metrics such as the mean bias error (MBE), mean absolute error (MAE) and standard deviation (StD) of the error of the bias are evaluated. The definition of these metrics are found in [33].…”