Piecewise Aggregate Approximation and Quantile Regression for Wind Speed Analysis

2017 ◽  
Author(s):  
Ronaldo R. B. de Aquino ◽  
Helen Barboza da Silva ◽  
Jonata C. de Albuquerque ◽  
Manuel Herrera ◽  
Aida A. Ferreira ◽  
...  
Keyword(s):  
Wind Speed ◽  
Quantile Regression

scholarly journals Probabilistic Wind Speed Forecasting based on Minimal Gated Unit and Quantile Regression

2020 ◽  
Vol 1659 ◽  
pp. 012039
Author(s):  
Tianyang Chen ◽  
Zheng Qian ◽  
Bo Jing ◽  
Jiangwen Wan ◽  
Fanghong Zhang
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Wind Speed Forecasting

scholarly journals Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 132 ◽  
Author(s):  
Lucky O. Daniel ◽  
Caston Sigauke ◽  
Colin Chibaya ◽  
Rendani Mbuvha
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Predictive Performance ◽  
Additive Models ◽  
Combination Method ◽  
Forecast Combination ◽  
Primary Input ◽  
Stochastic Gradient Boosting ◽  
Combination Methods ◽  
Forecasts Combination

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.

Wind speed forecasting based on Quantile Regression Minimal Gated Memory Network and Kernel Density Estimation

2019 ◽  
Vol 196 ◽  
pp. 1395-1409 ◽  
Author(s):  
Zhendong Zhang ◽  
Hui Qin ◽  
Yongqi Liu ◽  
Liqiang Yao ◽  
Xiang Yu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Wind Speed Forecasting ◽  
Memory Network

scholarly journals Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6125
Author(s):  
Lei Zhang ◽  
Lun Xie ◽  
Qinkai Han ◽  
Zhiliang Wang ◽  
Chen Huang
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Confidence Level ◽  
Kernel Density ◽  
Conditional Quantiles ◽  
Wind Speeds ◽  
Density Forecasting

Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

Method of Forecast Wind Speed Based on Wavelet Analysis and Quantile Regression

2013 ◽  
Vol 313-314 ◽  
pp. 1205-1209 ◽  
Author(s):  
Peng Lv ◽  
Cheng Fei Jiang ◽  
Bing Wei Cui
Keyword(s):  
Wind Speed ◽  
Wavelet Analysis ◽  
Quantile Regression ◽  
Wind Power ◽  
Model Prediction ◽  
Power Grid ◽  
Extreme Points ◽  
Arma Model ◽  
Better Than

Because of wind speed with the randomness and non-stationary properties, wind power has a great effect when it integrates to power grid. We can only prediction a more accurate wind speed to reduce its harmful of the power grid. However, it is difficult to make more accurately forecast because of its characteristics. This article we discuss a method that wind speed processed by wavelet at first, then giving a different quantile points forecast of each layer of wind speed decomposition using the quantile regression, finally we gain the differential forecast by reconstruction every layers predict. The result shows this method prediction effect is better than quantile regression forecast and ARMA model prediction, especially in the extreme points.

scholarly journals Quantile regression for analysing PM10 concentrations in Petaling Jaya

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Kar Yong Ng ◽  
Norhashidah Awang
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Predictor Variable ◽  
Nitrogen Monoxide ◽  
Positive Association ◽  
Ordinary Least Squares ◽  
Predictor Variables ◽  
Wind Component ◽  
West Wind ◽  
East West

Particulate matter with diameter less than 10µm (PM10) data usually exhibit different variations as they include normal days and pollution days. This paper applied quantile regression (QR) technique to inspect the changing relationship between predictor variables and PM10 concentrations at Petaling Jaya monitoring station in the year 2014 over different PM10 distributions. For comparative purpose, multiple linear regression (MLR) using ordinary least squares (OLS) estimation approach was also performed. The QR analysis results showed that the interrelationship between predictor variables and PM10 was not consistent across the PM10 quantile distributions and hence, proved discordancy with MLR estimates. The lagged PM10 concentration was the only important factor throughout the quantile distributions of PM10. It was found that the effects of lagged PM10, temperature, carbon monoxide (CO) increased from low to high quantile distributions, while the effects of lagged humidity, east-west wind component, wind speed and nitrogen monoxide (NO) showed the otherwise patterns. The lagged NO associated significantly with PM10 at low quantiles, whereas the lagged temperature and CO associated significantly at high quantiles only. Lagged humidity, east-west wind component and wind speed correlated significantly and negatively with PM10 at low and middle quantiles. Ozone (O3), however, had effect of changing nature from positive association at low PM10 distributions to negative association at high levels. Thus, QR is helpful to provide a more complete description of predictor variable effects on PM10 at different distributions, and may assist in PM10 management especially during haze periods.

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