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

Sun, Na; Zhou, Jianzhong; Lu, Chen; Jia, Benjun; Tayyab, Muhammad; Tian, Peng

doi:10.1016/j.energy.2018.09.180

Cited by 74 publications

(25 citation statements)

References 30 publications

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“…In our previous paper [14], an AVMD (adaptive variational mode decomposition) was proposed by adding a decomposition quality factor (QF) as the termination condition to automatic determine the key parameter ( the number of modes K) of VMD and thereby improve its decomposition performance. The AVMD is adopted to decompose the original series into several sub-series to make them easy to be predicted.…”

Section: The Framework and Process Of The Proposed Modelmentioning

confidence: 99%

“…The EEMD method is employed with the ensemble number of 100 and the white noise amplitude of 0.2 times standard deviation [12]. For AVMD, the number of modes is searched in the range [2,15] with increasing step of 1 [14]. For individual models, the number of hidden neurons of the ELM and BP neural networks are selected using grid search (GS) algorithm.…”

Section: B Parameter Settingsmentioning

confidence: 99%

“…Three mainstream indexes MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean squared error) are utilized to evaluate the performance of all the involved models in this study. Equations of these indices are shown as follow [14]: is the i-th forecasted wind speed or streamflow data, and N is the total number of all sample data.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In the VMD, the frequency center and bandwidth of each component are determined by searching the optimal solution of the variational model iteratively, so it can adaptively realize the division frequency domain of the signal and the effective separation of each component, highlight the local characteristics of the data. Recently, Sun et al [14] proposed an adaptive variational mode decomposition (AVMD) by adding a decomposition quality factor (QF) as the termination condition to automatic determine the key parameter ( the number of modes K) and then improve the performance of decomposition.…”

Section: Introductionmentioning

confidence: 99%

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A Composite Uncertainty Forecasting Model for Unstable Time Series: Application of Wind Speed and Streamflow Forecasting

et al. 2020

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Wind speed and streamflow series always are nonlinear and unstable because the effects of chaotic weather systems. These inherent features make them difficult to forecast, especially in a changing environment. To improve forecasting accuracy, an innovation uncertainty forecasting architecture is developed by coupling data decomposition method, feature selection, multiple artificial intelligence (AI) techniques and composite strategy to do unstable time series forecasting. In the designed architecture, the AVMD (adaptive variational mode decomposition) is first applied to excavate implicit information from the original time series. Then, the random forest is utilized to select the suitable inputs for each mode. After that, the GPR (Gaussian Process Regression), a very famous probabilistic AI technique, is driven by various neural networks (ELM (Extreme Learning Machine), BP (Back Propagation Neural Networks), GRNN(Generalized Regression Neural Networks) and RBF (Radial Basis Function Neural Networks)) to produce both deterministic and probabilistic forecasting results in a nonlinear manner to play strengths of each other. The effectiveness and applicability of the proposed approach is verified by unstable wind speed data and streamflow data, and also compared with eleven related models. Results indicate that the proposed model not only improves the forecasting accuracy for deterministic predictions, but also provides more probabilistic information for decision making. The proposed method achieves significantly better performance than the traditional forecasting models both on wind speed forecasting and streamflow forecasting with at least 50% average performance promotion over all the eleven competitors. Comprehensive comparisons demonstrate the superior performance of the proposed method than the involved models as a powerful tool for unstable series forecasting.

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Section: The Framework and Process Of The Proposed Modelmentioning

confidence: 99%

Section: B Parameter Settingsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Composite Uncertainty Forecasting Model for Unstable Time Series: Application of Wind Speed and Streamflow Forecasting

et al. 2020

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“…Therefore, combinations of these characteristics would produce improved predictions than those produced by any one of the single models used in combination. This methodology is applied in former education since combining forecasts of different models are either linear or non-linear combination methods [49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

et al. 2018

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Consistent streamflow forecasts play a fundamental part in flood risk mitigation. Population increase and water cycle intensification are extending not only globally but also among Pakistan’s water resources. The frequency of floods has increased in the last few decades in the country, which emphasizes the importance of efficient practices needed to adopt for various aspects of water resource management such as reservoir scheduling, water sustainability, and water supply. The purpose of this study is to develop a novel hybrid model for streamflow forecasting and validate its efficiency at the upper Indus basin (UIB), Pakistan. Maximum streamflow in the River Indus from its upper mountain basin results from melting snow or glaciers and climatic unevenness of both precipitation and temperature inputs, which will, therefore, affect rural livelihoods at both a local and a regional scale through effects on runoff in the Upper Indus basin (UIB). This indicates that basins receive the bulk of snowfall input to sustain the glacier system. The present study will help find the runoff from high altitude catchments and estimated flood occurrence for the proposed and constructed hydropower projects of the Upper Indus basin (UIB). Due to climate variability, the upper Indus basin (UIB) was further divided into three zone named as sub-zones, zone one (z1), zone two (z2), and zone three (z3). The hybrid models are designed by incorporating artificial intelligence (AI) models, which includes Feedforward backpropagation (FFBP) and Radial basis function (RBF) with decomposition methods. This includes a discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD). On the basis of the autocorrelation function and the cross-correlation function of streamflow, precipitation and temperature inputs are selected for all developed models. Data have been analyzed by comparing the simulation outputs of the models with a correlation coefficient (R), root mean square errors (RMSE), Nash-Sutcliffe Efficiency (NSE), mean absolute percentage error (MAPE), and mean absolute errors (MAE). The proposed hybrid models have been applied to monthly streamflow observations from three hydrological stations and 17 meteorological stations in the UIB. The results show that the prediction accuracy of the decomposition-based models is usually better than those of AI-based models. Among the DWT and EEMD based hybrid model, EEMD has performed significantly well when compared to all other hybrid and individual AI models. The peak value analysis is also performed to confirm the results’ precision rate during the flood season (May-October). The detailed comparative analysis showed that the RBFNN integrated with EEMD has better forecasting capabilities as compared to other developed models and EEMD-RBF can capture the nonlinear characteristics of the streamflow time series during the flood season with more precision.

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Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

Nachimuthu

Banerjee

Karuppaiah

2021

Concurrency and Computation

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Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi‐step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non‐linearity in the wind farm data and as well previous traditional and heuristic techniques has their own merits and demerits in performing the prediction process. This research study intends to handle the prevailing non‐linearity of the wind farm data and as well perform prediction of the parameters in a better manner with increased accuracy rate. The prediction study facilitates the renewable energy community to install the wind mills in the locations with higher accuracy rate and thereby power production gets increased extravagantly. The intelligent neural network developed in this article includes the ELMAN and spiking neuronal models with incorporated deep learning procedure and varying momentum factor criterion to achieve minimal error and better accuracy rate. Multi‐step forecasting is carried for 10‐min ahead and the numerical simulation executed with the proposed intelligent non‐linear forecasting techniques. The attained results confirm the superiority of the developed models over other techniques from previous works.

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

Cited by 74 publications

References 30 publications

A Composite Uncertainty Forecasting Model for Unstable Time Series: Application of Wind Speed and Streamflow Forecasting

A Composite Uncertainty Forecasting Model for Unstable Time Series: Application of Wind Speed and Streamflow Forecasting

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models

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