A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data. (November 2022)

• Record Type: Journal Article
• Authors: He, Boyu; Ye, Lin; Pei, Ming; Lu, Peng; Dai, Binhua; Li, Zhuo; Wang, Kaifeng
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 929
• Page End: 939
• Publication Date: 2022-11
• Subjects: Short-term wind power forecasting -- Numerical weather prediction -- Characteristic selection -- Long short-term memory -- Convolutional neural networks -- Induced ordered weighted average operator; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2021.10.102
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Due to the fluctuation and intermittency of wind power resources, large-scale wind power integration brings serious challenges to power systems. Among the existing short-term forecasting methods, the accuracy and forecast period length can hardly meet the demand of power system economic dispatching and day-ahead power purchase markets. To further enhance the accuracy and increase the time scale, a short-term wind power forecasting (WPF) combined model based on numerical weather prediction (NWP) analysis is presented in this paper. First, according to the criterion of the minimum redundancy maximum relevance (mRMR) algorithm, several factors are sifted from the NWP multivariate data. Second, different characteristics of the factors are extracted, and weather patterns are divided into different types on the basis of these characteristics. Third, two deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), are applied for short-term WPF respectively under different weather types. Furthermore, the forecast results of the two models are combined using the Induced Ordered Weighted Average (IOWA) operator. The actual data collected from a wind farm in Northwest China are used to verify the conclusions. Results show that the proposed method can forecast wind power under different weather circumstances and outperform existing Radial Basis Function (RBF), Extreme Learning Machine (ELM) and Support Vector Machine (SVM)-based methods with respect to forecasting accuracy.