基于多元自适应回归样条的光伏并网系统日输出功率预测

为了更加准确、灵活地预测光伏发电系统的输出功率,提出了基于多元自适应回归样条(MARS)的光伏系统输出功率预测方法。通过对该算法的原理进行分析,确定了模型分析流程,并介绍了数据来源。其次,以气温、日照时间等因素作为自变量,对MARS模型进行了分析研究,确定了光伏功率预测时的仿真模型。最后,将提出的预测方法与现有的预测方法进行了对比。通过训练数据以及测试数据对比分析各种方法的RMSE、MAD和MAPE,并根据历史数据预测光伏日输出功率。通过对比证实了MARS模型比其他模型更能准确预测光伏系统的输出功率。

Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines

At present,the prediction models of power output of a grid-connected photovoltaic system are mainly divided into linear and nonlinear models.The linear model is simple,easy to implement but less flexible.Because of the strong randomness of the output power of photovoltaic power generation system,the nonlinear model often provides better prediction.Given that,the output power prediction method of a photovoltaic system based on Multiple Adaptive Regression Spline(MARS)is proposed.This paper first analyzes the principle of the algorithm,determines the flow of model analysis,and introduces the data source of the photovoltaic system analyzed in this paper.Secondly,taking temperature,sunshine time and other factors as independent variables,the MARS model is analyzed and studied,and the simulation model for photovoltaic power prediction is determined.Finally,the prediction method proposed in this paper is compared with the existing prediction method.RMSE,MAD and MAPE of various methods are compared and analyzed through training data and test data,and photovoltaic daily output power is predicted based on historical data.The comparison proves that the MARS model is more accurate in predicting the output power of a photovoltaic system.