Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting

Abstract

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting.

Global Corrections to Reference Irradiance Spectra for Non-Clear-Sky Conditions

“…Research fields beyond environmental photochemistry also benefit from accurate surface level values of incident irradiance. In particular, the photovoltaic industry requires very accurate forecasts of solar irradiance, as abrupt variations in irradiance have the potential to degrade the quality of the photovoltaic power . Silicon-based solar cells are the most popular type of solar cell currently on the market and have a useable wavelength range of approximately 300–1100 nm.…”

“…In particular, the photovoltaic industry requires very accurate forecasts of solar irradiance, as abrupt variations in irradiance have the potential to degrade the quality of the photovoltaic power. 34 Silicon-based solar cells are the most popular type of solar cell currently on the market and have a useable wavelength range of approximately 300–1100 nm. This band gap means that researchers in the photovoltaic industry primarily use downward shortwave radiation (DSR) as a parameter in their incident irradiance models.…”

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

“…Due to meet the demand of power and maintain a balance between the supply and demand, always prediction process is carried out for the constructed solar farms so as to have a complete analysis on solar output power production and supply to the end users. Under this scenario, machine learning (ML) models are widely employed as black box models for performing the forecast mechanism of the solar PV output power [5][6][7][8][9][10][11][12][13] and this section of this research paper presents a detailed survey on different techniques and ML models applied over the years for predicting the PV output power.…”

“…• Occurrences of global minima and stagnation issues [3][4][5][6][7] • Scalability problems on the normalization procedures adopted [2,8,[12][13][14][15][16][17] • Over-fitting and under-fitting issues [5, 6, 9-11, 23, 48, 51] • Dimensionality constraints of the solar farm data and data handling issues [18][19][20][21][22][23][24] • Elapsed training time [29,31,37] • Data extraction problems in regression based ML models [10][11][12][13][14][15] • Higher number of trainable parameters in DL models [1, 14, 19-20, 26, 27, 43, 47] • Repetitive training of deep neural networks [19,20,26,27] • High computational overhead due to repetitive process [29][30][31][32][33][34][35][36] • Few predictor models with high complexity and data redundancy [45][46][47]…”

A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction

“…Neural networks are introduced into the field of wind speed prediction for their ability to fit the non-linear part of the data well. Neural network-based models can extract deeper features from wind speed data than traditional statistical models-for example, BP [7], RBF [8], artificial neural network [9], SVR [10], etc. To improve the learning ability and predictive ability of predictive models, deep neural networks are introduced into wind speed prediction, such as the deep belief network [11], RNN [12], GNN [13], and LSTM [14].…”