An Irradiation Prediction Model for Photovoltaic Power Generations Under Limited Weather Information

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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Short-term photovoltaic power generation prediction model based on fuzzy clustering-Elman neural network

2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP) ◽

10.1109/apap47170.2019.9225114 ◽

2019 ◽

Author(s):

MA jinhui ◽

ZHANG Jinjin ◽

LI zhi ◽

YE haifeng ◽

DING Jinjin

Keyword(s):

Neural Network ◽

Power Generation ◽

Prediction Model ◽

Fuzzy Clustering ◽

Short Term ◽

Elman Neural Network ◽

Model Based ◽

Photovoltaic Power

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Short-term prediction model of module temperature for photovoltaic power forcasting based on support vector machine

International Conference on Renewable Power Generation (RPG 2015) ◽

10.1049/cp.2015.0505 ◽

2015 ◽

Author(s):

Yujing Sun ◽

Fei Wang ◽

Zenggiang Mi ◽

Zhao Zhen ◽

Hongbin Sun ◽

...

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Support Vector ◽

Short Term ◽

Photovoltaic Power ◽

Term Prediction ◽

Short Term Prediction

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A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

Applied Sciences ◽

10.3390/app7040423 ◽

2017 ◽

Vol 7 (4) ◽

pp. 423 ◽

Cited By ~ 9

Author(s):

Jidong Wang ◽

Ran Ran ◽

Yue Zhou

Keyword(s):

Prediction Model ◽

Power Prediction ◽

Short Term ◽

Model Based ◽

Photovoltaic Power

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

Mathematical Problems in Engineering ◽

10.1155/2013/260351 ◽

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.

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Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

E3S Web of Conferences ◽

10.1051/e3sconf/202018501052 ◽

2020 ◽

Vol 185 ◽

pp. 01052

Author(s):

Runjie Shen ◽

Ruimin Xing ◽

Yiying Wang ◽

Danqiong Hua ◽

Ming Ma

Keyword(s):

Power Generation ◽

Time Series ◽

Random Forest ◽

Prediction Model ◽

Power Plants ◽

Time Series Prediction ◽

Cloud Amount ◽

Continuous Model ◽

Short Term ◽

Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

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Forecast of photovoltaic power generation based on DBSCAN

E3S Web of Conferences ◽

10.1051/e3sconf/202123602016 ◽

2021 ◽

Vol 236 ◽

pp. 02016

Author(s):

Jiaying Zhang ◽

Yingfan Zhang

Keyword(s):

Power Generation ◽

Prediction Model ◽

Power Plants ◽

Large Scale ◽

Clustering Algorithm ◽

Renewable Energy Sources ◽

Stable Operation ◽

Power Prediction ◽

Photovoltaic Power ◽

Bidirectional Lstm

The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.

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