Determination of the Concepts of Building a Solar Power Forecasting Model

Abstract

Since in Ukraine there are fines for imbalances in solar power generation in the “day-ahead” energy market, the forecasting of electricity generation is an important component of the solar power plant operation. To forecast the active power generation of photovoltaic panels, a mathematical model should be developed, which considers the main factors affecting the volume of energy generation. In this article, the main factors affecting the performance of solar panels were analysed using correlation analysis. The data sets for the construction of the forecasting model were obtained from the solar power plant in the Kyiv region. Two types of data sets were used for the analysis of factors and model building: 10-minute time interval data and daily data. For each data set, the input parameters were selected using correlation analysis. Considering the determining factors, the models of finding the function of reflecting meteorological factors in the volume of electricity generation are built. It is established that through models with a lower discreteness of climatic parameters forecast it is possible to determine the potential volume of electricity production by the solar power plant for the day-ahead with a lower mean absolute error. The best accuracy of the model for predicting electric power generation over the 10-minute interval is obtained in the ensemble random of a forest model. It is determined that models without solar radiation intensity parameters on the input have an unsatisfactory coefficient of determination. Therefore, further research will focus on combining a model of forecasting the day-ahead solar radiation with 10-minutes discreteness with a model for determining the amount of electricity generation. The determined predicted values of solar radiation will be the input parameter of the forecasting model described in the article

Розробка Графічного Інтерфейсу Для Короткострокового Прогнозування Сонячної Генерації На Основі Глибокого Навчання

“…Lower values of these metrics indicate higher forecast quality. For predicting the day ahead, the values of these errors are crucial as they indicate in absolute terms how much real data can deviate from the forecasted values [57].…”