Machine learning techniques for short-term solar power stations operational mode planning

Abstract

The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS. © The Authors, published by EDP Sciences.