Solar Power Prediction Based On Satellite Images And Support Vector Machine

This article proposes a new hybrid least squares support vector machine and artificial bee colony algorithm abc ls svm for multi hour ahead forecasting of global solar radiation ghi data.

Solar power prediction based on satellite images and support vector machine.

Some researchers used support vector machine svm and support vector regression svr for developing gsr predictor models. 2016 prediction of solar irradiation using quantum support vector machine learning algorithm. In this paper an overview on the various methodologies available for solar radiation prediction based on machine learning is presented. Extrapolation and statistical processes using satellite images or measurements on the ground level and sky images are generally suitable for short term forecasts up to 6 h.

Support vector machines. Smart grid and renewable energy 7 293 301. The authors in 11 presented satellite images and a support vector machine svm model to predict the solar irradiance and cloud movement. Solar energy is one of the most commonly used renewable energy resources.

The work in 12 presented an auto regression model to. In this paper a solar radiation prediction model has been developed for new alipore kolkata. This paper proposes a least square ls support vector machine svm based model for short term solar power prediction spp. In this paper we propose a solar power prediction model based on various satellite images and a support vector machine svm learning scheme.

Easily available meteorological parameters like temperature pressure and humidity have been utilized as inputs to build the prediction. The input of the model includes historical data of atmospheric transmissivity in a novel two dimensional 2d form and other meteorological variables including sky cover relative humidity and wind speed. The power output of these pv farms may fluctuate due to a wide variability of meteorological conditions and thus we need to compensate for this effect in advance. The inputs of the model were historical data on atmospheric transmissivity sky.

Zeng and qiao proposed a least square ls support vector machine svm based model for short term solar power prediction spp in the usa. Massive weather scenarios are obtained by deriving a conditional probability density function given a current weather prediction by using the bayesian theory. The generated weather scenarios are used as input variables to a machine learning based multi model solar power forecasting model where probabilistic solar power forecasts are obtained. The motion vectors of clouds are forecasted by.