A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

Abstract

Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction.

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

“…Single prediction models can be classified by complexity into simple nonlinear regression models, tree-based models, and DL models. Simple nonlinear regression models include support vector machine (SVM) [80], least squares SVM (LSSVM), support vector regression (SVR) [81,82], ELM [83], kernel ELM (KELM) [23,46], and various types of ANNs such as BPNN [84], radius basis function neural network (RBFNN), multilayer perceptron (MLP), wavelet neural network (WNN), and Elman neural network (ENN) [85]. Tree-based models encompass decision tree (DT), RF [86], gradient boosting decision tree (GBDT), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost) [87], and light gradient boosting machine (LightGBM) [88].…”

Interpolation of environmental data using deep learning and model inference

“…Antonić and Križan [19] applied ANN for spatiotemporal interpolation of climatic variables and found promising results. Other studies have equally applied ANN and other advanced machine-learning techniques for spatiotemporal interpolations of environmental data leading to satisfactory results [20][21][22][23][24][25][26][27][28][29][30]. Also, most of the existing methods utilize linear interpolation schemes or other linear techniques such as empirical orthogonal function analysis before applying ANN in spatiotemporal predictions of environmental data [31][32][33].…”

Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction

“…In addition to the mentioned methods, researchers have explored the use of fuzzy theory [8,9] and machine learning techniques, such as support vector machine (SVM) [10,11], for day-ahead wind forecasting. However, artificial neural network (ANN) has garnered significant attention and is widely employed for wind speed forecasting, either as a standalone model or as part of a hybrid approach in combination with other models, such as statistical models.…”