INCREASING THE ACCURACY OF SHORT-TERM FORECASTS OF PVSPP GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS AND TAKING ADDITIONAL FACTORS INTO ACCOUNT

Abstract

The paper focuses on the development of models for forecasting the electricity generation of industrial solar power plants using artificial neural networks and numerical weather prediction. The relevance of the research is driven by the need to reduce costs related to imbalances in electricity generation from renewable sources, which can sometimes reach 50% of the released electricity. Additionally, the imbalances of such producers are increasing in Ukraine's power system. Currently, the general imbalances of renewable energy producers in Ukraine have led to a 45% reduction in green electricity production, especially due to the damage or destruction of 75% of wind power plants and 15% of solar power plants in southern and southeastern regions as a result of hostilities. Increasing the accuracy and stability of electricity generation forecasts for such producers could significantly reduce costs associated with imbalances.. Various aggregation methods have been developed for 15-minute values of green energy generation to enhance forecasting accuracy for 1, 2, and 24-hour intervals. The study investigated the potential benefits of using numerical weather prediction (NWP) forecast values to enhance forecasting accuracy. The study revealed the significance of different factors for forecasting at each bias interval. The study employed two modern recurrent neural network models, LSTM and GRU, with varying time sequences. References 14, figures 5, table 2.