Short-and Medium-Term Photovoltaic Power Prediction based on STL-Former
The intermittency and volatility of photovoltaic(PV)power generation poses risks to the safety and stability of power grid operations.Accurately predicting PV power can effectively address this issue.Therefore,this paper proposed a short-and medium-term PV power prediction model based on STL-For-mer,which combined seasonal and trend decomposition using locally weighted regression(STL)and a neural network model.First,the STL-Former model utilized STL decomposition to expand the PV power data,extracting features based on historical sequences such as periodic and trend components.Then,the periodic and trend component features were concatenated with the original features,the data preprocess-ing and feature encoding were performed,and the power prediction was conducted using a neural network based on the Informer model.Finally,many experiments were carried out on real data sets.The experi-mental results show that STL-Former has high accuracy in short-and medium-term photovoltaic power fore-casting tasks.Among them,the average absolute value error is 0.176 and the mean square error is 0.180 in the task of PV power forecasting for 2 hours;the average absolute value error is 0.170 and the mean square error is 0.154 in the task of PV power forecasting for 28 hours.
deep learningphotovoltaic(PV)power predictionSTL decomposition
NETL
NSTL
万方数据
