摘要：The paper presents a short-term forecasting model for solar power stations (SPS) generation
developed by the authors. This model is based on weather data and built into the existing software product as a
separate short-term forecasting module for the SPS generation. The main problems associated with forecasting
the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the
error of the developed model but to the use of the same learning sample for both solar and cloudy days. This
paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and
representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation
example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based
on the actual weather provider data.
其他摘要：The paper presents a short-term forecasting model for solar power stations (SPS) generation developed by the authors. This model is based on weather data and built into the existing software product as a separate short-term forecasting module for the SPS generation. The main problems associated with forecasting the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the error of the developed model but to the use of the same learning sample for both solar and cloudy days. This paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based on the actual weather provider data.
