A Short-Term PV Power Forecasting Method Using a Hybrid Kmeans-GRA-SVR Model under Ideal Weather Condition

Lin, Jiemin; Li, Haiming

doi:10.4236/jcc.2020.811008

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…Lin et al faced the short-term PV power prediction problem and proposed a hybrid model based on the SVR model. The model could make more accurate predictions under ideal weather conditions through experimental analysis [8].…”

Section: Related Workmentioning

confidence: 99%

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

2024

SCPE

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An accurate short-term passenger flow forecast of rural tourism can avoid accidents as much as possible. However, the short-term passenger flow of rural leisure tourism shows nonlinear, seasonal, random, and other complex characteristics. Meanwhile, the traditional forecasting methods are often difficult to achieve accurate forecasting. Therefore, this study now used the nonlinear mapping function in the support vector regression to convert the passenger traffic training sample into a high-dimensional feature space and established a linear decision function. Then the influence of periodicity on the prediction effect through seasonal index adjustment was reduced. Finally, event triggering combined with core embedding technology for tamper-proof detection was adopted to improve the security of the platform. The results showed that the minimum absolute error of prediction with improved SVM model was 0.27% compared with traditional model and autoregressive integrated moving average model. After the introduction of the Internet search factor, the traffic prediction result was more accurate, which was 0.0425 smaller than that without the introduction of the Internet search factor. When the concurrency was less than 100 times/s, the average response time difference before and after adding the core embedded program was small, indicating that the security of page tampering technology was high. This research method can effectively predict the passenger flow of rural leisure tourism industry and ensure the safety of the platform.

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Section: Related Workmentioning

confidence: 99%

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

2024

SCPE

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“…The support vector machine prediction algorithm is trained by the data from similarity day. The experiment showed that the behavior of the algorithm was better than the single support vector machine [3]. Zhang et al established a PV power forecasting method on account of a fuzzy support vector machine that was simple and easy to understand, and the PV power generation was predicted with the prediction model [4].…”

Section: Introductionmentioning

confidence: 99%

Research on Photovoltaic Power Prediction Based on BP Neural Network Algorithm

Zhang

Wei²,

et al. 2023

J. Phys.: Conf. Ser.

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The output power of photovoltaic (PV) power plant is influenced by the fluctuation of meteorological conditions, which, coupled with the intermittent nature of the power output, has raised concerns about the stability of power grid. According to the variations in meteorological conditions, PV power prediction technology can predict the PV output power in advance to decrease the influence of PV power generation on the stable operation of the power grid. In this paper, the PV power prediction model is built on Matlab simulation software with a BP neural network algorithm. After that, the power prediction calculation of a PV power station is carried out under the operation conditions of sunny and cloudy days. The results show that, in the periods of prediction, the power curve of the predicted value is close to the power curve of the real value, which demonstrates the great accuracy of the BP prediction model established in this paper.

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“…This is done to get a more optimal prediction model [7]. One of them is using Hybrid K-means grey Relational Analysis and Support Vector Regression to predict short-term PV power [8]. The results obtained an accurate PV power prediction model with RMSE 0.9608.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term

Adam

Silalahi

Aprillia

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Photovoltaic (PV) technology is a renewable technology utilizing conversion of solar power or solar radiation into electrical energy. In the manufacture of Solar Power Generation systems, reference is needed regarding the cost of generation and scheduling of maintenance plans. To obtain this reference, it is necessary to predict the photovoltaic power output which is used to determine the power output of PV in the future. In this study, a system that is used to predict short-term power output in PV is designed. This system uses solar irradiation data and 42 days of power output in off-grid PV mini-grid as the dataset. The dataset obtained from the PV output is processed using the Support Vector Regression method with the Kernel Radial Basis Function (RBF) function. Based on the dataset used, this study succeeded in testing the best kernel, namely the RBF kernel. Evaluation of the prediction model obtained a smaller error value than other kernel tests with a Mean Absolute Percentage Error (MAPE) value of 21.082%, Mean Square Error (MSE) value of 0.122, and Mean Absolute Error (MAE) value of 0.262. The prediction model obtained is used to predict the short-term PV power output for the next 3 days. The results of the prediction model have an error value of 5.785 % for MAPE, 0.005 for MAE and 0.069 for MSE. Therefore, the predictive model can be categorized as very good and feasible to predict short-term power output

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A Short-Term PV Power Forecasting Method Using a Hybrid Kmeans-GRA-SVR Model under Ideal Weather Condition

Cited by 11 publications

References 36 publications

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

Research on Photovoltaic Power Prediction Based on BP Neural Network Algorithm

Support Vector Regression Method for Predicting Off-Grid Photovoltaic Output Power in the Short Term

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