With the development of science and technology and the progress of human society, the demand for renewable energy continues to increase, and the production of photovoltaic energy develops rapidly, but the production of photovoltaic energy is affected by many factors, such as temperature, light intensity, wind speed, geographical location Wait. The purpose of this paper is to study the short-term prediction method based on the organic Rankine cycle, propose a differential autoregressive moving average (ARIMA) model and a combined sequence-to-sequence (Seq2Seq) model, and on this basis, enter the short-term Interval forecasting method of photovoltaic power generation. Compared with a single model, the improvement of the proposed method is reflected in the use of the Seq2Seq model in the recurrent neural network to be good at capturing data normality to make up for the shortcomings of the ARIMA model itself, so as to achieve complementary advantages between the two. Both models and full interval forecasts. Under sunny weather conditions, when the confidence level is 95%, the method in this paper is used to construct the photovoltaic power generation power prediction interval. The average width percentage of the PIMWP interval value reaches the minimum value of 15.3, and the coverage percentage interval PICP also reaches 92.1%. This method can be used to carry out interval forecast.

Manyank Biseen. Short-term Forecasting Method of Solar Photovoltaic Power Generation Based on Organic Rankine Cycle. Academic Journal of Energy (2022), Vol. 3, Issue 2: 40-47. https://doi.org/10.38007/RE.2022.030205.

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