A Power Prediction Method for Photovoltaic Power Station Based on Neutral Network Using Numerical Weather Information

2013 ◽  
Vol 724-725 ◽  
pp. 3-9 ◽  
Author(s):  
Hong Lu Zhu ◽  
Jian Xi Yao
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Prediction Model ◽  
Prediction Method ◽  
Weather Forecast ◽  
Power Prediction ◽  
Power Station ◽  
Numerical Weather ◽  
Photovoltaic Power ◽  
Solar Radiation Intensity

As the installed capacity of photovoltaic power station is growing, the power prediction techonology is of great important to reduce the random damage to the power system. A prediction model using neural network is proposed in the paper, the solar radiation model is adopt to ensure the accuracy of the prediction results in clear sky contions.Through the analysis of photovoltaic power station output power influence factors, the the solar radiation intensity, humidity and temperature are chosen as the input of the neural network prediction model.At the same time, in order to improve accuracy the photovoltaic power station power prediction model, the power adopt numerical weather forecast information. And the prediction model is tested by the photovoltaic power station historical operation data, and the short-term power prediction has a good performance.

scholarly journals Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

2021 ◽  
Vol 252 ◽  
pp. 01056
Author(s):  
Qiang Zhang ◽  
Gang Liu ◽  
Xiangzhong Wei
Keyword(s):  
Neural Network ◽  
Output Power ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Power Prediction ◽  
Power Station ◽  
Short Term ◽  
Factors Affecting ◽  
Photovoltaic Power

Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.

scholarly journals Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Dongxiao Niu ◽  
Yanan Wei ◽  
Yanchao Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Power Prediction ◽  
Adaptive Neural Network ◽  
Scene Simulation ◽  
Knowledge Mining ◽  
Photovoltaic Power ◽  
Model Combining ◽  
Neural Network Prediction ◽  
Network Prediction

Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent. Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system. The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power. According to the proposed scene simulation knowledge mining (SSKM) technique, the influencing factors are clustered and fused into prediction model. Combining adaptive algorithm with neural network, adaptive neural network prediction model is established. Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.

Photovoltaic Power Prediction Model Based on Weather Forecast

2019 ◽  
Author(s):  
Wei Huang ◽  
Chao Zhang ◽  
Xinhe Zhang ◽  
Junxia Meng ◽  
Xiaohua Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Weather Forecast ◽  
Power Prediction ◽  
Model Based ◽  
Photovoltaic Power

scholarly journals Photovoltaic Power Prediction Considering the Influence of Smog on Solar Radiation

2021 ◽  
Vol 299 ◽  
pp. 02007
Author(s):  
Tianlong Gao
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Solar Radiation ◽  
Atmospheric Aerosols ◽  
Rbf Neural Network ◽  
Power Prediction ◽  
Photovoltaic Panel ◽  
Factors Affecting ◽  
Photovoltaic Power ◽  
The Impact

As smog significantly weakens the intensity of solar radiation, the impact of smog on photovoltaic power generation cannot be ignored. This article aims to improve the prediction accuracy of photovoltaic power generation under smog weather. The impact of main atmospheric meteorological factors on atmospheric aerosols under smog weather is studied, and radial basis function neural network is adopted to predict the optical thickness of atmospheric aerosols; then, the inclined plane radiation model is established to predict the radiation intensity received by the photovoltaic panel; finally, considering fully the factors affecting the photovoltaic power generation under the smog weather, the RBF neural network is used to predict the photovoltaic power. Experimental verification proved that the presented photovoltaic power prediction model has high accuracy.

Research on the Power Prediction of Photovoltaic Power Station Based on LMS Adaptive Filter

2013 ◽  
Vol 433-435 ◽  
pp. 464-468
Author(s):  
Hong Lu Zhu ◽  
Jian Xi Yao
Keyword(s):  
Adaptive Filter ◽  
Prediction Method ◽  
Energy Output ◽  
Power Prediction ◽  
Power Station ◽  
Stochastic Fluctuation ◽  
Continuous Increase ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Lms Filter

Along with continuous increase of capacity of PV(photovoltaic) power station, techniques for power prediction of PV power station play an important role in reducing impact of stochastic fluctuation of PV power stations energy output on power system. The paper proposes a method for power prediction of PV power station based on LMS adaptive filter, a FIR approach model of PV station power prediction model based on LMS adaptive filter is established with history runtime value of PV station as the input value of filter and current value as the expected value. The advantage of using LMS filter to power prediction of PV power station is that a real-time, explicit identification result can be obtained as well as that the algorithm is simple. A test has been made with runtime data of one PV power station and the result showed that the prediction method in the paper has good accuracy in terms of super-short term power prediction.

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Energy ◽  
2020 ◽  
pp. 119692
Author(s):  
Xiaosheng Peng ◽  
Hongyu Wang ◽  
Jianxun Lang ◽  
Wenze Li ◽  
Qiyou Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Power Prediction ◽  
Numerical Weather ◽  
Model Based ◽  
Wind Power Prediction
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