A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

Lin, Jiemin; Li, Haiming

doi:10.4236/jcc.2021.911007

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…When forecasting samples, the support vector regression algorithm has a fast operation speed, high forecasting accuracy, and fewer parameters to be adjusted. So, its application field and development prospects are extensive [28][29][30].…”

Section: Support Vector Regression (Svr) Modelmentioning

confidence: 99%

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Fan¹,

Hui-zhen²,

Chen³

et al. 2022

Glob. J. Energ. Technol. Res. Updat.

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With the continuous expansion of the capacity of photovoltaic power generation systems, accurate power generation load forecasting can make grid dispatching more reasonable and optimize load distribution. This paper proposes a combined forecasting model based on Auto Regression Integrate Moving Average (ARIMA), back propagation neural network (BPNN), and support vector regression (SVR), namely ARIMA-BPNN-SVR model, aiming at the problem of low accuracy of a single model and traditional forecasting model. Through the complementary advantages of ARIMA, BPNN, and SVR models, the model has good anti-noise ability, nonlinear mapping, and adaptive ability when processing photovoltaic power generation data. Data experiments are carried out on solar photovoltaic power generation in the United States, and the accuracy of model forecasting is evaluated according to MAE, MSE, RMSE, and MAPE. The experimental results show that the proposed ARIMA-BPNN-SVR outperforms the forecasting performance of the single models ARIMA, BPNN, and SVR. Its MAE, MSE, RMSE and MAPE are 0.53, 0.41, 0.64 and 0.84 respectively. In the Wilcoxon sign-rank test, the p-value of the proposed model reached 0.98, indicating the effectiveness of the ARIMA-BPNN-SVR model.

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Section: Support Vector Regression (Svr) Modelmentioning

confidence: 99%

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Fan¹,

Hui-zhen²,

Chen³

et al. 2022

Glob. J. Energ. Technol. Res. Updat.

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“…erefore, this paper introduces an adaptive k-means, which can automatically set the number of clusters according to the input data set. Its main idea is an iterative process based on distance [30]. e steps of k-means algorithm are as follows:…”

Section: Theoretical Basis Of Photovoltaic Power Predictionmentioning

confidence: 99%

Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

Liu

2022

Mathematical Problems in Engineering

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In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according to the short-term photovoltaic power generation characteristics. And the adaptive k-means is used to cluster the initial training set and the photovoltaic power on the forecast day. The Gru model is trained on the initial training set data of each category, and the generated power is predicted in combination with the trained Gru model. Finally, considering three typical weather types, the proposed method is used for simulation analysis and compared with the other three traditional photovoltaic power generation single prediction models. The comparison results show that the proposed short-term photovoltaic power generation prediction method based on an adaptive k-means and Gru network has better effect, better robustness, and less error.

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“…Literature [8] uses Artificial Fish-Swarm Algorithm (AFSA) and Back Propagation (BP) neural network to predict photovoltaic power, and verified the effectiveness of the model through experiments. The prediction model built by deep learning method is more accurate than the traditional method, but the current neural network prediction method can not meet the prediction demand in the face of the complex data characteristics [9] and scale of photovoltaic power generation. How to better combine the appropriate intelligent optimization algorithm to optimize the model and form a combined model with both prediction efficiency and prediction accuracy has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Short term photovoltaic power prediction based on FWA-CNN-GRU neural network

Wang

Zhao

et al. 2022

J. Phys.: Conf. Ser.

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In order to achieve accurate prediction of short-term photovoltaic power generation, this paper establishes a photovoltaic power prediction model based on FWA-CNN-GRU through the combination and optimization of neural network. Firstly, this paper crunches data and establishes the test sets and training sets. Then, the prediction model based on CNN-GRU is built through Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) neural network. Meanwhile, the Fireworks Algorithm (FWA) is introduced to search the optimal value of the super parameters of the network model. A prediction model that is based on FWA-CNN-GRU is established. Finally, the prediction is made according to different seasons. The experimental comparison shows that the average Root Mean Square Error and Mean Absolute Error of the optimized model are reduced by 9.190% and 6.095% respectively, and the average coefficient of determination reaches 96.83%. It shows that FWA-CNN-GRU model has higher prediction accuracy and can provide a theoretical basis for the operation and regulation of power grid.

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A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

Cited by 5 publications

References 31 publications

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Photovoltaic Power Generation Forecasting Based on the ARIMA-BPNN-SVR Model

Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

Short term photovoltaic power prediction based on FWA-CNN-GRU neural network

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