A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression

Naik, Jyotirmayee; Dash, P.K.; Dhar, Snehamoy

doi:10.1016/j.renene.2019.01.006

Cited by 111 publications

(24 citation statements)

References 38 publications

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“…4: end for 5: Apply FST approach to determine the compromise solutions O * according to Eqs. (32) and (33). 6: while t < iter max do 7:…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…It is easy to obtain the solution using one criterion when only one objective solution in single-objective optimization problem should be optimized. However, in multi-objective optimization problem, more than one criterion for optimization under conflicting targets needs to be solved simultaneously, thus, there exists a POF solution set [7], [33]. The multi-objective optimization problem can be equivalent to minimization problem expressed as Eqs.…”

Section: B the Solving Methods Of The Multi-objective Optimizationmentioning

confidence: 99%

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Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability

Sun

Wei

et al. 2020

IEEE Access

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In recent years, a large number of wind power has been applied in the micro-grid (MG). Influenced by randomness characteristics of wind speed, the uncertainty in the power output of wind turbines imposes some safety and stability problems on the optimal energy management in MG. To address this problem, an expert energy management system (EEMS) considering wind power probability is developed in this study for optimal dispatching of a typical grid-connected MG. The EEMS composes of wind power probabilistic forecasting module, multi-objective optimization module and energy storage system (ESS) module. In the wind power forecasting module, wind power probabilistic forecasting based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Gaussian process regression (GPR) is proposed in this study. To improve the forecasting results, CEEMDAN, an effective signal processing method, is employed to decompose the wind power data, then, the decomposed subseries are utilized as the inputs of GPR for probabilistic forecasting. A two-step solution methodology combining an efficient and effective improved multi-objective bat algorithm (IMOBA) with fuzzy set theory (FST) is put forward to solve the optimal dispatching problems. In the first step, IMOBA is developed to optimize the energy dispatching of EEMS by minimizing both economic cost and pollutant emissions simultaneously, and obtain a well-distributed set of Pareto optimal front (POF), then, FST is employed to identify the best compromise solutions from POF. Six operational scenarios of a typical grid-connected MG based one-POF-one-day and one-POF-one-hour dispatching schemes are constructed to investigate the effectiveness of the proposed strategy and provide more flexibility for decision makers. The results illustrate that EEMS can effectively schedule power generation and energy storage by considering economic cost and pollutant emission objectives simultaneously. INDEX TERMS Wind power probabilistic forecasting, multi-objective optimization, micro-grids, bat algorithm, expert energy management system. ANG LI received the B.Sc. degree from Shanghai University, Shanghai, China, in 2013, where she is currently pursuing the Ph.D. degree in control science and engineering. Her research interests include positioning in wireless networks, algorithm optimization, and neuro-engineering.

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“…4: end for 5: Apply FST approach to determine the compromise solutions O * according to Eqs. (32) and (33). 6: while t < iter max do 7:…”

Section: Simulation and Resultsmentioning

confidence: 99%

Section: B the Solving Methods Of The Multi-objective Optimizationmentioning

confidence: 99%

Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability

Sun

Wei

et al. 2020

IEEE Access

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“…Due to the complexity and variety of the measured signals, the difficulty and key to using the VMD algorithm is how to select the appropriate decomposition number K and the penalty parameter value α [ 37 ]. When using VMD, the preset decomposition number K and the penalty parameter α need to be selected.…”

Section: Improved Adaptive Vmd Algorithmmentioning

confidence: 99%

Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy

Song

Yao

et al. 2019

Entropy

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The normal operation of high-pressure common rail injector is one of the important prerequisites for the healthy and reliable operation of diesel engines. Therefore, this paper studies the high-precision fault diagnosis method for injectors. Firstly, this paper chooses VMD to adaptively decompose the common rail fuel pressure wave. The biggest difficulty in VMD decomposition is the need to manually set the internal combination parameters K and α. In order to overcome this shortcoming, this paper proposes an improved fruit fly search. The variational mode decomposition method of the algorithm, with the energy growth factor e as the objective function, can adaptively decompose the multi-component signal into superimposed sub-signals. In addition, based on the analytic hierarchy process and dispersion entropy, hierarchical dispersion entropy is proposed to obtain a comprehensive and accurate complexity estimation of time series. Then, a fault diagnosis scheme for high-pressure common rail injector based on IFOA-VMD and HDE is proposed. Finally, using the engineering test data, the method is compared with other methods. The proposed method appears, based on the numerical examples, to be better from both a computational and classification accuracy point of view.

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“…Therefore, uncertainty analysis is particularly important for the topic of unsteady energy series forecasting. The most commonly used methods for probabilistic forecasting to construct the prediction intervals (PIs) are traditionally Bayesian, mean-variance, and Bootstrap [28]. These approaches have their own characteristics.…”

Section: Introductionmentioning

confidence: 99%

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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A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression

Cited by 111 publications

References 38 publications

Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability

Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability

Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy

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

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