Improving the Accuracy of the Forecast of Electricity Production by Photovoltaic Power Station Based on the Random Forest Method
DOI:
https://doi.org/10.31649/1997-9266-2024-177-6-52-61Keywords:
photovoltaic power station, generation, forecasting, regression analysis, machine learning, data analysisAbstract
The work examines the methods used to forecast energy production by Photovoltaic Power Station (PPS), as well as ways to improve the accuracy of the forecast to optimize the structure of the electricity balance in the power system. The research is aimed at identifying effective forecasting approaches and algorithms, assessing their accuracy and reliability. Based on the results of the study, a combination of regression analysis and machine learning methods is proposed, which provides acceptable forecast accuracy for planning power reserves in the power system. The Random Forest method was used as a basis, as it ensures adaptability to the specifics of energy generation by PPS in different regions of Ukraine and in different periods of the year. To increase the efficiency of machine learning, an algorithm for pre-filtering datasets using autoregression and moving average methods was proposed. This allows better preparation of input data for further use in forecasting, ensuring smoothing of time series and elimination of random fluctuations that can negatively affect forecast accuracy. The use of pre-filtering methods allows to identify the main regularities in the data, which, in turn, increases the accuracy of machine learning models. The Random Forest method was not chosen by chance: it is well suited for forecasting tasks where many different factors need to be taken into account, which may affect the results differently depending on time and region. This is especially important in the case of forecasting the energy generation of solar power plants, where production is significantly affected by variables such as cloud cover, temperature, seasonality, etc. Using Random Forest allows you to take into account non-linear dependencies and interactions between factors, which helps to increase the accuracy of forecasts. The use of Random Forest as the main machine learning algorithm is due to its flexibility and ability to adapt to different conditions, which allows you to effectively take into account the features of energy generation in different regions and during different seasons.
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