Power prediction of mechanism-data hybrid drive photovoltaic power plant based on TOPSIS-GRNN
The article addresses the problem of relatively low accuracy of traditional PV power prediction and proposes a hybrid TOPSIS-GRNN based mechanism-data driven PV plant power prediction model.Firstly,the correlation analysis of several meteorological indicators and the output power of PV power plant is carried out,and the meteorological data with high correlation is selected as the input factor of the model.The TOPSIS algorithm was used to select the optimal similar days,and then the theoretical values of their PV plant output power and meteorological data were used to build the GRNN prediction model.Finally,the model was simulated and validated by combining the historical meteorological data and power data on the DKASC website.The final test results yielded an average power prediction accuracy of 0.8269 kW for RMSE,3.45%for MAPE and 0.019 5 kW for MAE.The prediction accuracy of this forecasting method is significantly higher than that of a single forecasting model and has some theoretical and practical value.
photovoltaic power predictionTOPSISbest similar dayGRNN
国家科技期刊平台
NSTL
万方数据
维普
