A novel hybrid model based on Laguerre polynomial and multi-objective Runge–Kutta algorithm for wind power forecasting

Ye, Jiahao; Xie, Lei; Ma, Li; Bian, Yifan; Xu, Xun

doi:10.1016/j.ijepes.2022.108726

Cited by 14 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the effectiveness of the proposed multi-objective forecasting, scenario generation, and decision scheduling integrated stochastic optimal scheduling model, this section conducts extensive integrated experiments on the HMORUN algorithm, the HMORUN-HPLNN wind and photovoltaic power forecasting model, and the HMORUN-MOSSP scheduling model. Firstly, the performance of the HMORUN algorithm is tested on the ZDT test suite [24] and compared with other advanced multi-objective algorithms. Secondly, the HMORUN-HPLNN wind power and photovoltaic power forecasting models were tested, including HMORUN-HPLNN forecasting performance testing and comparison of multi-model forecasting results.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…So far, most wind-photovoltaic forecasting models are singleobjective forecasting models, which only consider forecasting accuracy and ignore the impact of improving the stability of forecasting results on the OSPS. Considering both forecasting accuracy and stability can allow the forecasting model to capture more wind-photovoltaic stochastic features so as to ensure more accurate wind-photovoltaic forecasts at the spikes, which can provide more accurate scheduling instructions for OSPS [24]. Therefore, it is necessary to develop new wind-photovoltaic multi-objective forecasting models to provide higher-quality forecasting data for scenario generation, better track the uncertain characteristics of wind-photovoltaic, and ensure the secure and stable operation of the power system.…”

Section: Research Gaps and Questionsmentioning

confidence: 99%

“…This study uses stability index (SDEX), mean absolute error (MAE), root mean square error (RMSE), index of agreement (IA), and median absolute percentage error (MdAPE) as evaluation indicators of the forecasting model to comprehensively evaluate the forecasting accuracy and forecasting stability of the HMORUN-HPLNN forecasting model [24]. SDEX reflects the degree of stability of the model forecast.…”

Section: B Performance Criteriamentioning

confidence: 99%

“…To verify the performance of the proposed HMORUN algorithm, it was compared with advanced multi-objective algorithms such as MOALO [42], MODA [43], NSGAIII [44], MOSSA [45], and MORUN [24] on the ZDT test suite. The ZDT test suite consists of four benchmark problems (ZDT1-ZDT4) with 10 dimensions, and their objective functions are presented in Table 1.…”

Section: A Testing Of Hmorunmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

Ye,

Xie,

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

In the optimization scheduling of power systems containing renewable energy, to ensure that the scheduling results have higher security and better economy, this study proposes a multi-objective forecasting, scenario generation, and decision scheduling integrated stochastic optimal scheduling method. Firstly, to improve the accuracy and stability of wind-photovoltaic power forecasting, a novel multiobjective wind-photovoltaic forecasting model is proposed based on the Laguerre polynomial, pseudoinverse learning, and hybrid multi-objective Runge-Kutta algorithm (HMORUN). Secondly, to deal with wind-photovoltaic uncertainty, scenarios with representative wind-photovoltaic uncertainty characteristics are generated by scenario generation and reduction techniques using wind-photovoltaic power forecast results. Finally, considering wind-photovoltaic output fluctuations, the degree of source-load matching, wind-photovoltaic utilization, and system economic efficiency factors, with the objective of maximizing the tracking of the load curve and minimizing system economic costs, a stochastic optimized scheduling model for power systems is established. This study uses HMORUN as a solution tool for the multi-objective stochastic optimization scheduling problem (MOSSP). This study uses constraint repair techniques to deal with the complex constraints of the MOSSP model to avoid system load shedding and minimize wind and photovoltaic generation curtailment. To verify the effectiveness of the proposed model, a 10-generator power system, including a wind farm and a photovoltaic plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety and better economy.INDEX TERMS Laguerre polynomial, Pseudo-inverse learning, Forecasting, Hybrid multi-objective Runge-Kutta algorithm, Stochastic optimal scheduling.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Research Gaps and Questionsmentioning

confidence: 99%

Section: B Performance Criteriamentioning

confidence: 99%

Section: A Testing Of Hmorunmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

Ye,

Xie,

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, a large number of scholars have studied wind power prediction models, which can be mainly divided into physical models [ 4 ], statistical models [ 5 ], artificial intelligence (AI) models [ 6 ], and hybrid models [ 7 ]. The physical models are based on the method of fluid mechanics, which uses numerical weather prediction data to calculate the wind turbine output curve and then calculate wind power from it [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer

Tian

Wang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction.

show abstract

Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting

Ribeiro,

da Silva,

Moreno

et al. 2024

Appl Intell

View full text Add to dashboard Cite

A novel hybrid model based on Laguerre polynomial and multi-objective Runge–Kutta algorithm for wind power forecasting

Cited by 14 publications

References 44 publications

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

Multi-Scenario Stochastic Optimal Scheduling for Power Systems With Source-Load Matching Based on Pseudo-Inverse Laguerre Polynomials

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer

Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting

Contact Info

Product

Resources

About