Abstract:

In order to find a more accurate photovoltaic power prediction method to be suitable for arid areas, based on the actual observed data and numerical forecasting information from a photovoltaic power station in Dunhuang, Gansu Province in 2022, three short-term photovoltaic power forecasting models were established by using the prototype prediction method, short-term error correction method and stepwise regression method. At the same time, a grasshopper optimization algorithm was used to optimize the three single models to form a combined prediction method. The forecasting effects of the four methods were tested and evaluated. The results show that the root-mean-square error and relative root-mean-square error of the stepwise regression method are smaller than those of the prototype prediction method and the short-term error correction method, and the prediction accuracy of the stepwise regression method is higher, the fluctuation range is smallest, and the prediction effect is more stable. Compared with the three single models, the combined prediction model formed by the grasshopper optimization algorithm has improved the prediction effect, and the average root-mean-square error is reduced by 145.21, 153.48 and 70.91 kW, respectively. Under different weather conditions, the combined forecasting model is superior to the single forecasting model, and the forecasting effect is the best under sunny weather condition.

Key words: photovoltaic power prediction, prototype forecasting method, short-term error correction method, stepwise regression method, combined prediction, grasshopper optimization algorithm

摘要：

为了找出适用于干旱区的更为精确的光伏功率预测方法，基于2022年甘肃省敦煌某光伏电站的实际观测数据和数值预报资料，利用原型预报法、短期误差订正法、逐步回归法建立3种短期光伏功率预测模型，同时，使用蝗虫优化算法对3种单一模型优化形成组合预测方法，并对4种方法的预报效果进行检验和评估。结果表明，单一预测模型中逐步回归法均方根误差、相对均方根误差均小于原型预报法、短期误差订正法，逐步回归法的预报精度更高，变化幅度最小，预报效果更为稳定；与3种单一模型相比，经过蝗虫优化算法形成的组合预测模型预报效果有所提升，平均均方根误差分别降低145.21、153.48和70.91 kW；不同天气状况下，组合预测模型均优于单一预测模型，晴天预测效果最好。

关键词: 光伏功率预测, 原型预报法, 短期误差订正法, 逐步回归法, 组合预测, 蝗虫优化算法