Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs

Hong, Yi; Chang, Huei-Lin; Chiu, Chang-Hsuan

doi:10.1016/j.energy.2010.05.041

Cited by 106 publications

(34 citation statements)

References 21 publications

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“…In their research, the results demonstrate that the performance of the ANN model outperforms the ARMA model. Hong et al [24] propose a Multilayer Feed-forward Neural Network (MFNN) to predict wind speed at different time horizons. The MFNN model has a better performance as compared with the traditional forecasting models.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

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Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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“…The literature cites numerous investigations into the use of Neural Networks for wind speed prediction (More and Deo 2003, Reikard 2008, Wu et al 2009, Hong et al 2010, Anvari Moghaddam and Seifi 2011, De Giorgi et al 2011, Shi et al 2011 Hatziargyriou 2012) but the majority of the work done to date uses historical wind speed data as the only (meteorological) parameter to train the networks. Very few exceptions exist to this with some adding other parameters such as ambient temperature and humidity when predicting wind speed (Cali et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Influence of neural network training parameters on short-term wind forecasting

Brka

Al-Abdeli

Kothapalli

2014

International Journal of Sustainable Energy

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This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of Neural Networks are used to forecast power generated via specific horizontal axis wind turbines.Meteorological data used is for a specific Western Australian location.Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the Neural Network deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of this data, the length of training pattern within the overall span which are used to implement the predictions and whether the inclusion of solar irradiance data can appreciably affect wind speed prediction accuracy. There appears to be a relatively complex relationship between these factors and the accuracy of wind speed prediction via Neural Networks. Predicting wind speed based on Neural Networks trained using wind speed and solar irradiance data also increases the prediction accuracy of wind power generated, as can the type of network selected.

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“…The physical models use numerical weather prediction (NWP) to predict wind speed and then input the data into wind power output models to obtain the output power [4]. The common statistical forecast methods include the time series method [5,6], artificial neural network (ANN) method [7,8], and support vector machine (SVM) [9]. The main focus of these methods is to reduce the point forecast errors of wind power by introducing new models.…”

Section: Introductionmentioning

confidence: 99%

A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization

et al. 2017

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Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed to further improve wind power prediction performance. First, in the designed prediction interval model, the input variables are identified based on attribute significance using rough set theory. Next, the Naive Bayesian Classifier (NBC) is established to obtain the prediction power class. Finally, the upper and lower output weights of NBC are optimized segmentally by PSO, and are used to calculate the upper and lower bounds of the optimal prediction intervals. The superiority of the proposed approach is demonstrated by comparison with a Naive Bayesian model with fixed output weight, and a rough set-Naive Bayesian model with fixed output weight. It is shown that the proposed rough set-Naive Bayesian-particle swarm optimization method has higher coverage of the probabilistic prediction intervals and a narrower average bandwidth under different confidence levels.

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Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs

Cited by 106 publications

References 21 publications

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Influence of neural network training parameters on short-term wind forecasting

A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization

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