A review of international experience in forecasting renewable energy generation using machine learning methods

Abstract

In this work, we conduct an analytical review of contemporary international approaches to forecasting the volume of electricity generated by renewable energy sources, as well as to investigate current problems and prospective solutions in this field. The existing forecasting methods were classified following an analysis of published literature on the development of forecasting models, including those based on physical, statistical and machine learning principles. The application practice of these methods was investigated to determine the advantages and disadvantages of each method. In the majority of cases, particularly when carrying out short-term forecasting of renewable electricity generation, machine learning methods outperform physical and statistical methods. An analysis of the current problems in the field of weather data collection systems allowed the major obstacles to a wide application of machine learning algorithms to be determined, which comprise incompleteness and uncertainty of input data, as well as the high computational complexity of such algorithms. An increased efficiency of machine learning models in the task of forecasting renewable energy generation can be achieved using data preprocessing methods, such as normalization, anomaly detection, missing value recovery, augmentation, clustering and correlation analysis. The need to develop data preprocessing methods aimed at optimizing and improving the overall efficiency of machine learning models for forecasting renewable energy generation was justified. Research in this direction, while taking into account the above problems, is highly relevant for the imp lementation of programs for the integration of renewable energy sources into power systems and the development of carbon-free energy.