This thesis describes the development of algorithms, which can forecast the coverage of the Sun by the clouds. In times of increased renewable source installements on the grid, the importance of short term forecasting is becoming more relevant by the day, for production of electricity. It also represents the basis on which short term energy traders work on. In this diploma tehsis we are trying to find a solution spcifically for solar plants, which are capable of stressing the power grid in case of weather changes. Likewise, the program could also be useful to energy traders.
This literature describes every step of the development. Firstly, camera calibration or gathering of extrinsic parameters of the camera is described, which is done to help the program remap the objects on the sky from a semicircle onto a flat surface. Next step in the development of the program was to set thresholds of HSV values for cloud and sun centre detecting purposes. With detected centres the development could then be focused on a tracking algortihms. With two consecutive screenshots of the sky, the program is able to guess which centres on both screenshots belong to the same detected object, based on Euclidian formula for distance. When the program detects and saves six consecutive collections of centres it can then start with the position forecasting of the sun and the clouds. While program is reading the video of the sky, all the centres of clouds and the sun are being recorded (every sixth list of coordinates is being updated upon arrival of new frames of the sky), for forecasting method data feeding purposes. Forecasting method was chosen based on the best curve fitting capability. Comparison of a few forecasting methods is also described, among those are: ARIMA, exponential smoothing, polynomial regression and a method which is based on the average distance between the collection of corresponnding coordinates. Best among those methods proved to be Exponential Smoothing (DES), which was able to predict the coordinates most accurately based on a one-step ahead (1 minute) test. After all the forecasting, the program draws a sun and after that it draws all the clouds onto a screenshot of the sky. Sun is drawn with a color that must be different from the colors with which the clouds are being drawn with. Program then checks how many pixels are left with the same color as the sun, to then calculate the coverage by the clouds. At the end program also iterates through every pixel that represents the clouds over the sun and calculates their color ratios. Based on those ratios it puts every pixel into one of three classes that descirbe the transparency of the cloud. One of the classes describe dark pixel, another one describes semi dark and the last one represents the light ones.Transparency is the average of all those pixels.
Front-end development of the program is also described towards the end of the paper.
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