Construction and Research of Ultra-Short Term Prediction Model of Solar Short Wave Irradiance Suitable for Qinghai–Tibet Plateau

Abstract

The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the power grid. Therefore, constructing a suitable ultra-short-term prediction model for the Tibetan Plateau region holds significant importance. This study was based on the autoregressive integrated moving average model (ARIMA), random forest model (RF), and long short-term memory model (LSTM) to construct a prediction model for forecasting the average irradiance for the next 10 min. By locally testing and optimizing the model parameter, the study explored the applicability of each model in different seasons and investigates the impact of factors such as training dataset and prediction time range on model accuracy. The results showed that: (1) the accuracy of the ARIMA model was lower than the persistence model used as a reference model, while both the RF model and LSTM model had higher accuracy than the persistence model; (2) the sample size and distribution of the training dataset significantly affected the accuracy of the models. When both the season (distribution) and sample size were the same, RF achieved the highest accuracy. The optimal sample sizes for ARIMA, RF, and LSTM models in each season were as follows: spring (3564, 1980, 4356), summer (2772, 4752, 2772), autumn (3564, 3564, 4752), and winter (3168, 3168, 4752). (3) The prediction forecast horizon had a significant impact on the model accuracy. As the forecast horizon increased, the errors of all models gradually increased, reaching a peak between 80 and 100 min before slightly decreasing and then continuing to rise. When both the season and forecast horizon were the same, RF had the highest accuracy, with an RMSE lower than ARIMA by 65.6–258.3 W/m2 and lower than LSTM by 3.7–83.3 W/m2. Therefore, machine learning can be used for ultra-short-term forecasting of solar irradiance in the Qinghai–Tibet Plateau region to meet the forecast requirements for solar power generation, providing a reference for similar studies.