Short-Term Wind Speed and Output Power Forecasting Based on WT and LSSVM

Liu, Dejun; Li, Hui

doi:10.1109/iciecs.2009.5365740

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…The rationale behind the decomposition-based approach rests on the simpler seasonal patterns of the subseries that can be modeled by regression algorithms with less difficulty. Wavelet decomposition (WD) [28], [29], empirical mode decomposition (EMD) [30], [31] and variational mode decomposition (VMD) [25], [32] are popular decomposition approaches. For a thorough discussion of decompositionbased hybrid models for wind power forecasting, we refer interested readers to [33].…”

Section: A Related Workmentioning

confidence: 99%

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

2021

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Wind power is one of the most efficient renewable resources without emissions. Nonetheless, it is difficult to exactly forecast wind power generation given historical power and wind speed information, the failure of which may cost the risk of large-scale outages. This article takes a close look at the artificial recurrent neural network framework in the application of wind power forecasting. More intelligent mechanisms using attention to capture spatial-temporal patterns within historical data are emphasized in this work and are shown to be state-of-the-art for short-term wind power forecasting. Our experiments at a wind farm in southeast Australia using only the historical wind power generation and wind speed records from ambient weather stations show that, e.g., 7.4750% in mean absolute error (MAE) and 0.3345 in the coefficient of variation in the root mean squared error (CV-RMSE) for half-hour-ahead prediction. To interpret how the three models under consideration-the long-and short-term time-series network (LSTNet), the temporal pattern attention-based long short-term memory (TPA-LSTM) and the dual-stage attention-based recurrent neural network (DA-RNN)-work, we visualize and analyze the details of the models so that further improvement can be made by combining the advantageous components of the models.

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Section: A Related Workmentioning

confidence: 99%

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

2021

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“…We considered persistence method, multivariate ARIMA, radial basis function (RBF) neural network, multilayer perceptron (MLP) neural network, and ridgelet neural network ridgelet (RNN), all developed by Amjady et al in [5] for comparison since Amjady et al used the same training and test data for their models. Moreover, the leassquares support vector machines (LSSVMs) are also used for comparison, as they are powerful time series prediction methods and have been employed for wind power forecasting [22]. The persistence method is a common benchmark approach used for comparison in wind power forecasting [3,4].…”

Section: Wind Power Forecasting For Sotavento Wind Farmmentioning

confidence: 99%

“…The RNN approach proposed in [5] is a three-layer neural network model with fixed weighting parameters between the hidden layer and output node and ridgelets as activation functions. The LSSVMs, which have been employed for wind power forecasting [22], are used for comparison as well. We also developed the local linear neurofuzzy (LLNF) model (note that if the order of polynomials in the LNF model is fixed at 1, then the LLNF model is resulted as a special case), trained by LOLIMOT algorithm, in order to evaluate improvement in LNF model's accuracy achieved through POLYMOT learning algorithm.…”

Section: Wind Power Forecasting For Sotavento Wind Farmmentioning

confidence: 99%

Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

Faghihnia

Salahshour

Ahmadian

et al. 2014

Advances in Mathematical Physics

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Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

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“…The results were compared with the persistence method, and it was found that NF provided better accuracy compared to the latter model. LSSVR was also extensively applied in solving many wind energy problems in recent years [33][34][35][36][37][38][39][40]. Zhang et al [33] applied the LSSVR model for wind energy prediction, compared with the RBF model, and found that LSSVR provided better results than RBF.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [34] used a model combination of ARIMA, extreme learning machine, SVR, and LSSVR for wind speed prediction. Liu and Li [36] predicted short-term wind speed and wind power by utilizing LSSVR with wavelet transform (WT). The results were compared with a recursive least square (RLS) regression model, and the LSSVR-WT gave better results than the RLS-WT model.…”

Section: Introductionmentioning

confidence: 99%

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

et al. 2019

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Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

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Short-Term Wind Speed and Output Power Forecasting Based on WT and LSSVM

Cited by 8 publications

References 12 publications

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

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