Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model
Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/12/2322 |
| _version_ | 1798024219630501888 |
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| author | Wei Sun Qi Gao |
| author_facet | Wei Sun Qi Gao |
| author_sort | Wei Sun |
| collection | DOAJ |
| description | Wind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input−output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models; meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system. |
| first_indexed | 2024-04-11T17:58:52Z |
| format | Article |
| id | doaj.art-9d57f590666344ee9d50afe5776c7210 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T17:58:52Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9d57f590666344ee9d50afe5776c72102022-12-22T04:10:35ZengMDPI AGEnergies1996-10732019-06-011212232210.3390/en12122322en12122322Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization ModelWei Sun0Qi Gao1Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, ChinaDepartment of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, ChinaWind power, one of renewable energy resources, is a fluctuating source of energy that prevents its further participation in the power market. To improve the stability of the wind power injected into the power grid, a short-term wind speed predicting model is proposed in this work, named VMD-P-(ARIMA, BP)-PSOLSSVM. In this model, variational mode decomposition (VMD) is combined with phase space reconstruction (P) as data processing method to determine intrinsic mode function (IMF) and its input−output matrix in the prediction model. Then, the linear model autoregressive integrated moving average model (ARIMA) and typical nonlinear model back propagation neural network (BP) are adopted to forecast each IMF separately and get the prediction of short-term wind speed by adding up the IMFs. In the final stage, particle swarm optimization least squares support vector machine (PSOLSSVM) uses the prediction results of the two separate models from previous step for the secondary prediction. For the proposed method, the PSOLSSVM employs different mathematical principles from ARIMA and BP separately, which overcome the shortcoming of using just single models. The proposed combined optimization model has been applied to two datasets with large fluctuations from a northern China wind farm to evaluate the performance. A performance comparison is conducted by comparing the error from the proposed method to six other models using single prediction techniques. The comparison result indicates the proposed combined optimization model can deliver more accurate and robust prediction than the other models; meanwhile, it means the power grid dispatching work can benefit from implementing the proposed predicting model in the system.https://www.mdpi.com/1996-1073/12/12/2322short-term wind speed productionvariational mode decompositionphase space reconstructionautoregressive integrated moving average modelback propagation neural networkparticle swarm optimization least squares support vector machine |
| spellingShingle | Wei Sun Qi Gao Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model Energies short-term wind speed production variational mode decomposition phase space reconstruction autoregressive integrated moving average model back propagation neural network particle swarm optimization least squares support vector machine |
| title | Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model |
| title_full | Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model |
| title_fullStr | Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model |
| title_full_unstemmed | Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model |
| title_short | Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model |
| title_sort | short term wind speed prediction based on variational mode decomposition and linear nonlinear combination optimization model |
| topic | short-term wind speed production variational mode decomposition phase space reconstruction autoregressive integrated moving average model back propagation neural network particle swarm optimization least squares support vector machine |
| url | https://www.mdpi.com/1996-1073/12/12/2322 |
| work_keys_str_mv | AT weisun shorttermwindspeedpredictionbasedonvariationalmodedecompositionandlinearnonlinearcombinationoptimizationmodel AT qigao shorttermwindspeedpredictionbasedonvariationalmodedecompositionandlinearnonlinearcombinationoptimizationmodel |