Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information

Eseye, Abinet Tesfaye; Zhang, Jianhua; Zheng, Dehua

doi:10.1016/j.renene.2017.11.011

Cited by 315 publications

(103 citation statements)

References 18 publications

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“…Thus, only 25 data points, i.e., those for 1991–2015, are available for use in building the model, with the resulting characteristic of a ‘small sample’. Hence, models with large samples, such as the ARIMA(autoregressive integrated moving average model), RBFNN(radial basis function neural networks), ELM(extreme learning machine) and SVM(support vector machine) [23–29] are not suitable. Because the stability of 25 data points cannot guarantee that it can continue along the existing state "inertia" in the future, the mean and variance of the data series will change obviously when the data points are less than 30.…”

Section: Data Characteristics and Methods Selectionmentioning

confidence: 99%

An improved gray prediction model for China’s beef consumption forecasting

Zeng

Meng

et al. 2019

PLoS ONE

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To balance the supply and demand in China's beef market, beef consumption must be scientifically and effectively forecasted. Beef consumption is affected by many factors and is characterized by gray uncertainty. Therefore, gray theory can be used to forecast the beef consumption, In this paper, the structural defects and unreasonable parameter design of the traditional gray model are analyzed. Then, a new gray model termed, EGM(1,1, r ), is built, and the modeling conditions and error checking methods of EGM(1,1, r ) are studied. Then, EGM(1,1, r ) is used to simulate and forecast China’s beef consumption. The results show that both the simulation and prediction precisions of the new model are better than those of other gray models. Finally, the new model is used to forecast China’s beef consumption for the period from 2019–2025. The findings will serve as an important reference for the Chinese government in formulating policies to ensure the balance between the supply and demand for Chinese beef.

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Section: Data Characteristics and Methods Selectionmentioning

confidence: 99%

An improved gray prediction model for China’s beef consumption forecasting

Zeng

Meng

et al. 2019

PLoS ONE

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“…Thi Thom Hoang et al proposed an SVM classifier based on a differential PSO for the purpose of monitoring surge arrester conditions . Abinet Tesfaye Eseye et al proposed a hybrid forecasting model combining wavelet transformation and PSO for short‐term (one‐day‐ahead) generation power forecasting of a real microgrid photovoltaic system . In the present study, PSO is used as an optimization technique to optimize the SVM parameters for identifying pipeline leakage from different abnormal working conditions and to optimize the SVR parameters for pipeline leakage localization.…”

Section: Introductionmentioning

confidence: 99%

“…21 Abinet Tesfaye Eseye et al proposed a hybrid forecasting model combining wavelet transformation and PSO for short-term (one-dayahead) generation power forecasting of a real microgrid photovoltaic system. 22 In the present study, PSO is used as an optimization technique to optimize the SVM parameters for identifying pipeline leakage from different abnormal working conditions and to optimize the SVR parameters for pipeline leakage localization. This paper presents a novel pipeline leakage accident identification and localization approach integrating an advanced, self-developed FBG hoop strain sensor with an SVM (SVR) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

Jia

Ren

et al. 2018

Struct Control Health Monit

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Summary A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise‐free situation. The anti‐noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.

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“…(1) Statistical methods mainly include time series methods [4], wavelet analysis [5,6], classification regression [7,8], and spectral analysis [9]. These methods use statistical principles to establish a functional relationship between historical power series and future photovoltaic power.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

2019

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Short-term photovoltaic power forecasting is of great significance for improving the operation of power systems and increasing the penetration of photovoltaic power. To improve the accuracy of short-term photovoltaic power forecasting, an ensemble-model-based short-term photovoltaic power prediction method is proposed. Firstly, the quartile method is used to process raw data, and the Pearson coefficient method is utilized to assess multiple features affecting the short-term photovoltaic power output. Secondly, the structure of the ensemble model is constructed, and a k-fold cross-validation method is used to train the submodels. The prediction results of each submodel are merged. Finally, the validity of the proposed approach is verified using an actual data set from State Power Investment Corporation Limited. The simulation results show that the quartile method can find outliers which contributes to processing the raw data and improving the accuracy of the model. The k-fold cross-validation method can effectively improve the generalization ability of the model, and the ensemble model can achieve higher prediction accuracy than a single model.

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Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information

Cited by 315 publications

References 18 publications

An improved gray prediction model for China’s beef consumption forecasting

An improved gray prediction model for China’s beef consumption forecasting

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

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