Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm

2017 ◽  
Vol 151 ◽  
pp. 709-722 ◽  
Author(s):  
Xi-wei Mi ◽  
Hui Liu ◽  
Yan-fei Li
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Correction Algorithm ◽  
Wind Speed Forecasting ◽  
Forecasting Method ◽  
Learning Machine

An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine

Energy ◽  
2018 ◽  
Vol 165 ◽  
pp. 939-957 ◽  
Author(s):  
Na Sun ◽  
Jianzhong Zhou ◽  
Lu Chen ◽  
Benjun Jia ◽  
Muhammad Tayyab ◽  
...  
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Adaptive Dynamic ◽  
Learning Machine

scholarly journals A hybrid structure of an extreme learning machine combined with feature selection, signal decomposition and parameter optimization for short-term wind speed forecasting

2018 ◽  
Vol 42 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Feng Zhu ◽  
Dajun Du
Keyword(s):  
Feature Selection ◽  
Wind Speed ◽  
Extreme Learning Machine ◽  
Parameter Optimization ◽  
Gravitational Search Algorithm ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Data ◽  
Hybrid Particle Swarm Optimization ◽  
Learning Machine

Influenced by various environmental and meteorological factors, wind speed presents stochastic and unstable characteristics, which makes it difficult to forecast. To enhance the forecasting accuracy, this study contributes to short-term multi-step hybrid wind speed forecasting (WSF) models using wavelet packet decomposition (WPD), feature selection (FS) and an extreme learning machine (ELM) with parameter optimization. In the model, the WPD technique is applied to decompose the empirical wind speed data into different, relatively stable components to reduce the influence of the unstable characteristics of wind speed. A hybrid particle swarm optimization gravitational search algorithm (HPSOGSA) combining conventional PSOGSA with binary PSOGSA (BPSOGSA) is utilized to realize the FS and parameter optimization simultaneously. The PSOGSA is employed to tune the parameter combination of input weights and biases in ELM, while BPSOGSA is exploited to select the most suitable features from the candidate input variables determined by a partial autocorrelation function for reconstruction of the input matrix for ELM. The proposed forecasting strategy carries out multi-step short-term WSF using mean half-hour historical wind speed data collected from a wind farm situated in Anhui, China. To investigate the forecasting results of the hybrid model, a lot of comparisons and analyses are executed. Simulation results illustrate that the proposed WPD-ELM model with FS and parameter optimization can effectively catch the non-linear characteristics hidden in wind speed data and provide satisfactory WSF performance.

scholarly journals Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jicheng Quan ◽  
Li Shang
Keyword(s):  
Feature Selection ◽  
Wind Speed ◽  
Extreme Learning Machine ◽  
Electricity Market ◽  
Feature Selection Method ◽  
Bayesian Optimization ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Proposed Model ◽  
Learning Machine

Short-term wind speed forecasting is crucial to the utilization of wind energy, and it has been employed widely in turbine regulation, electricity market clearing, and preload sharing. However, the wind speed has inherent fluctuation, and accurate wind speed prediction is challenging. This paper aims to propose a hybrid forecasting approach of short-term wind speed based on a novel signal processing algorithm, a wrapper-based feature selection method, the state-of-art optimization algorithm, ensemble learning, and an efficient artificial neural network. Variational mode decomposition (VMD) is employed to decompose the original wind time-series into sublayer modes. The binary bat algorithm (BBA) is used to complete the feature selection. Bayesian optimization (BO) fine-tuned online sequential extreme learning machine (OSELM) is proposed to forecast the low-frequency sublayers of VMD. Bagging-based ensemble OSELM is proposed to forecast high-frequency sublayers of VMD. Two experiments were conducted on 10 min datasets from the National Renewable Energy Laboratory (NREL). The performances of the proposed model were compared with various representative models. The experimental results indicate that the proposed model has better accuracy than the comparison models. Among the thirteen models, the proposed VMD-BBA-EnsOSELM model can obtain the best prediction accuracy, and the mean absolute percent error (MAPE) is always less than 0.09.

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