A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

Hu, Mei-Chih; Hu, Zhijian; Yue, Jingpeng; Zhang, Menglin; Hu, Man

doi:10.3390/en10040419

Cited by 33 publications

(19 citation statements)

References 26 publications

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“…In [17], a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models was presented to quantify potential risks of wind power series. In [18], a prediction model was established through a kernel extreme learning machine. A key issue in these methods is how to select reasonable trained data to obtain a high-precision intelligent model that approximates the nonlinear relationship between input and output variables.…”

Section: Many Physical and Statistical Prediction Methods Have Been Pmentioning

confidence: 99%

A Reliability Assessment Approach for Electric Power Systems Considering Wind Power Uncertainty

Yang

Liu

et al. 2020

IEEE Access

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The intermittence and uncertainty of wind power pose challenges to large-scale wind power grid integration. The study of wind power uncertainty is becoming increasingly important for power system planning and operation. This paper proposes a wind power probabilistic interval prediction model, and a novel reliability assessment approach is presented for electrical power systems. First, the unknown parameters estimation of the autoregressive integrated moving average (ARIMA) prediction model is based on the Markov chain Monte Carlo (MCMC)-based Bayesian estimation method to improve the quality of statistical inference. Then, a quantum genetic algorithm is used to segment the power to determine the best output for each power segment weight and calculate the probabilistic prediction interval of wind power. Finally, reliability assessment by the sequential Monte Carlo simulation is presented combining with the probabilistic prediction interval of wind power on IEEE-RTS79 reliability test system. The simulation results that proposed variation range of reliability assessment indices consider the uncertain scenario of wind power and has guiding significance for power generation scheduling. Compared with genetic algorithm and particle swarm optimization algorithm, it is proved that the proposed prediction interval model has better prediction interval coverage probability index and interval average bandwidth index. INDEX TERMS Bayesian estimation, interval prediction, reliability index, sequential Monte Carlo method, wind power.

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Section: Many Physical and Statistical Prediction Methods Have Been Pmentioning

confidence: 99%

A Reliability Assessment Approach for Electric Power Systems Considering Wind Power Uncertainty

Yang

Liu

et al. 2020

IEEE Access

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“…Other approaches regarding the prediction intervals of renewable resources, the price of energy, and the electricity demand have been reported ( Hu, Hu, Yue, Zhang, & Hu, 2017;Li et al, 2018;Shrivastava et al, 2015Shrivastava et al, , 2016Voyant et al, 2018 ). In the works of Shrivastava et al (2016) and Shrivastava et al (2015) , methodologies were proposed based on the support vector machine (SVM) to generate the prediction intervals for wind speed and electricity costs.…”

Section: Literature Review For Prediction Interval Modellingmentioning

confidence: 99%

“…However, the upper and lower values for the training process were unknown; they were artificially generated by modifying the training values within a given percentage. Hu et al(2017) used the kernel extreme learning machine (KELM) method to develop the prediction interval for wind power using data from two wind farms. The artificial bee colony algorithm was used to find the parameters necessary for the KELM models.…”

Section: Literature Review For Prediction Interval Modellingmentioning

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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“…Figure 3 to Figure 8 depict the results in different case interval forecasting. In this paper, three common indices forecasting interval coverage percentage (FICP), forecasting interval average width (FIAW) and mean absolute percentage error (MAPE) were used to assess the effect of the interval forecasting [24,27]. Table 2 to Table 4 give the different case interval forecasting results and analysis.…”

Section: Case Studymentioning

confidence: 99%

A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM

et al. 2019

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The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value.

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A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

Cited by 33 publications

References 26 publications

A Reliability Assessment Approach for Electric Power Systems Considering Wind Power Uncertainty

A Reliability Assessment Approach for Electric Power Systems Considering Wind Power Uncertainty

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

A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM

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