Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Zang, Haixiang; Li, Fan; Mian, Guo; Wei, Zhinong; Sun, Guoqiang; Zhang, Li

doi:10.1155/2016/8760780

Cited by 14 publications

(10 citation statements)

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“…Further, Ref. [53] proposed a short-term wind power interval forecasting model based on EEMD, RVM and Runs Test (RT). This model used the RT method to reconstruct the EEMD generated IMF components and obtained three new components (Trend, Detailed and Random) characterized by the fine-to-coarse order.…”

Section: Eemd-rvm Modelsmentioning

confidence: 99%

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A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

et al. 2019

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Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

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Section: Eemd-rvm Modelsmentioning

confidence: 99%

“…The overall prediction was obtained with a combination of prediction outcomes of all components. Table 15 shows the percentage of improvement in models proposed in [52,53] as compared to the simple RVM models.…”

Section: Eemd-rvm Modelsmentioning

confidence: 99%

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

et al. 2019

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“…e EEMD proposed by Wu and Huang [42] is an effective noise-aided method that can handle nonlinear and nonstationary time series. It has been widely used in wind speed forecasting [43,44], aircraft auxiliary power unit (APU) degradation prediction [45], turbine fault trend prediction [46], and rolling bearing fault diagnosis [47]. It has shown a good effect on enhancing the prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

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“…Two conditions should be satisfied when the EEMD approach is used: (1) The mean of the upper and lower envelopes must be equal to zero everywhere, and (2) the number of extreme data and the number of zero crossing must be equal or differ at most by one [24,25]. EEMD can decompose the time series into several IMFs, and an IMF means a simple oscillatory mode with a variable amplitude and frequency over time [26,27]. A detailed description of the process of extracting the IMF modes can be found in [28][29][30].…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Monthly Streamflow Forecasting Using EEMD-Lasso-DBN Method Based on Multi-Scale Predictors Selection

Chen

Wei

Qiu

2018

Water

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For the inherent characteristics of a raw streamflow times series and the complicated relationship between multi-scale predictors and streamflow, monthly streamflow forecasting is very difficult. In this paper, an method was proposed integrating the ensemble empirical mode decomposition (EEMD), least absolute shrinkage and selection operator (Lasso) with deep belief networks (DBN) for forecasting monthly streamflow time series, which is EEMD-Lasso-DBN (ELD) method. To develop the ELD model, the raw streamflow time series was resolved into different elements, including intrinsic mode functions (IMFs) and residue series, using the EEMD technique. The predictors of each IMF element and residue were screened using the Lasso technique from a large number of candidate predictors, respectively. Then, the DBN models were built to simulate the complex relationship between the resolved elements and the selected predictors, respectively. The predicted results of the IMFs and residual series were assembled as an ensemble forecast for the raw streamflow time series and were compared with the other models. The monthly streamflow series from Tennessee, in the USA, were investigated using the ELD method. It was found that each IMF has different characteristics and physical meaning, corresponding to different predictors. The proposed ELD model can significantly improve the accuracy of monthly streamflow forecasting.

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Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Cited by 14 publications

References 50 publications

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Monthly Streamflow Forecasting Using EEMD-Lasso-DBN Method Based on Multi-Scale Predictors Selection

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