Interval Deep Generative Neural Network for Wind Speed Forecasting

Khodayar, Mahdi; Wang, Jianhui; Manthouri, Mohammad

doi:10.1109/tsg.2018.2847223

Cited by 136 publications

(50 citation statements)

References 34 publications

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“…The long-term and medium-term wind power predictions do not require very high forecasting accuracy. The short-term WPP forecasts the wind power in next three days and needs more precise results [7], while the ultra-short-term WPP, whose temporal resolution is 15 min, predicts the wind power in next 4 h and requires the highest precision [1]. It is very difficult to accurately forecast wind power because of its chaotic and stochastic characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…It is very difficult to accurately forecast wind power because of its chaotic and stochastic characteristics. Additionally, compared to other WPP, the ultra-short-term WPP is more difficult due to its shorter time frames [7]. In the past decades, extensive efforts have focused on WPP, and a large number of wind power prediction methods, models, and tools have been developed.…”

Section: Related Workmentioning

confidence: 99%

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Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

2018

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In order to improve the accuracy of wind power prediction (WPP), we propose a WPP based on multivariate phase space reconstruction (MPSR) and multivariate linear regression (MLR). Firstly, the multivariate time series (TS) are constructed through reasonable selection of wind power and weather factors, which are closely associated with wind power. Secondly, the phase space of the multivariate time series is reconstructed based on the chaos theory and C-C method. Thirdly, an auto regression model for multivariate phase space is created by regarding phase variables as state variables, and the very-short-term wind power is predicted by using a multi-linear regression algorithm. Finally, a parallel algorithm based on map/reduce is presented to improve computing speed. A cloud computing platform, Hadoop consisting of five nodes, is established as a matter of convenience, followed by the prediction of wind power of a wind farm in the Hunan province of China. The experimental results show that the model based on MPSR and MLR is more accurate than both the continuous method and the simple approximation method, and the parallel algorithm based on map/reduce effectively accelerates the computing speed.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

2018

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“…(2) Data-driven methods predict the wind speed within a few hours through a pattern analysis, and are trained based on past data. Such methods are more suitable for forecasting wind speed values within a short period of time because the amount of past data continuously increases [5]. (3) Hybrid methods employ a prediction approach through the application of a statistical prediction after acquiring weather forecast data using physical methods and a combination of both physical and statistical approaches [6].…”

Section: Introductionmentioning

confidence: 99%

“…(5), where t i , t f , and t O are the input, forget, and output gates, respectively, and ~t C is a new candidate value for the cell state. The LSTM cell acts as an accumulator of the state information, and an update of the old cell state 1 t C  into the new cell state t C is applied.…”

mentioning

confidence: 99%

Support Vector Machine and Long Short-term Memory using Multivariate Models for Wind Power Forecasting

Kang,

Son*

2020

IJITEE

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Renewable energy has recently gained considerable attention. In particular, interest in wind energy is rapidly increasing globally. However, the characteristics of instability and volatility in wind energy systems also have a significant on power systems. To address these issues, numerous studies have been carried out to predict wind speed and power. Methods used to forecast wind energy are divided into three categories: physical, data-driven (statistical and artificial intelligence methods), and hybrid methods. In this study, among artificial intelligence methods, we compare short-term wind power using a support vector machine (SVM) and long short-term memory (LSTM). The method using an SVM is a short-term wind power forecast that considers the wind speed and direction on Jeju Island, whereas the method using LSTM does not consider the wind speed and direction. As the experiment results indicate, the SVM method achieves an excellent performance when considering the wind speed and direction.

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“…In recent decades, several methodologies have been proposed to solve nonlinear model identification problems that use a finite number of measured data and consider an optimality criterion ( Škrjanc, 2011 ). Many studies have examined methods for improving the accuracy of these approaches to obtain higher precision in expected value prediction ( Khodayar, Wang, & Manthouri, 2018;Kroll & Schulte, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Marín

Cruz

Sáez

et al. 2019

Expert Systems with Applications

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Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval in terms of the information about the uncertainty modelled for a higher prediction horizon. In this paper, a new prediction interval modelling methodology based on fuzzy numbers is proposed to solve the abovementioned drawbacks. Fuzzy and neural network prediction interval models are developed based on this proposed methodology by minimizing a novel criterion that includes the coverage probability and normalized average width. The fuzzy number concept is considered because the affine combination of fuzzy numbers generates, by definition, prediction intervals that can handle uncertainty without requiring assumptions about the data distribution. The developed models are compared with a covariance-based prediction interval method, and high-quality intervals are obtained, as determined by the narrower interval width of the proposed method. Additionally, the proposed prediction intervals are tested by forecasting up to two days ahead of the load of the Huatacondo microgrid in the north of Chile and the consumption of the residential dwellings in the town of Loughborough, UK. The results show that the proposed models are suitable alternatives to electrical consumption forecasting because they obtain the minimum interval widths that characterize the uncertainty of this type of stochastic process. Furthermore, the information provided by the obtained prediction interval could be used to develop robust energy management systems that, for example, consider the worst-case scenario.

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Interval Deep Generative Neural Network for Wind Speed Forecasting

Cited by 136 publications

References 34 publications

Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

Ultra-Short-Term Wind Power Prediction Based on Multivariate Phase Space Reconstruction and Multivariate Linear Regression

Support Vector Machine and Long Short-term Memory using Multivariate Models for Wind Power Forecasting

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

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