Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power Forecasts

Abstract

A new statistical approach has been proposed for improved short-term wind-electric power forecasts of wind power plants (WPPs) based on a new wind-pattern-recognition technique, and reference wind mast (RWM) data correlations with numerical weather predictions (NWPs) to localize wind data to the given WPP site. For this purpose, first, NWP data are combined using adaptive boosting (AdaBoost) machine-learning algorithm to provide a proper combination of meteorological grid data from a set of grids around each WPP. Then, combined grid data are clustered, and for each cluster, an artificial neural network (ANN)/support vector machine (SVM) model is constructed to learn the relationship between the wind patterns of NWP data and RWM measurements. The outputs of this wind-to-wind modeling stage are used to obtain raw short-term 48-h-ahead wind-electric power forecasts via the consecutive statistical ANN/SVM models applied in the wind-to-power stage. The systematic errors are eliminated by applying model output statistics (MOS) and a weighted average combination method is used to obtain the final 48-h-ahead wind-electric power forecasts. The proposed model has been successfully applied to seven WPPs with installed capacities in the range from 10 to ${\sim}200\;\text{MW}$. The wind-electric power-forecast results obtained by using the proposed approach are compared with the reference benchmark models and other statistical models, which do not use any wind pattern recognition or NWP data correction via RWM measurements. It has been shown that the proposed wind-pattern-recognition technique and the wind-to-wind model developed by the use of an RWM for each WPP bring an improvement in the range from 2.3% to 5.1% on the normalized mean absolute error of wind-electric power forecasts, for an average training period of 2 years and a test period of 6 months for the given WPPs, as compared with conventional statistical methods.