Research on data-driven smart grid photovoltaic energy prediction method
Photovoltaic(PV)energy,as a core component of the global renewable energy system,has a fluctuating capacity that is signif-icantly affected by climatic conditions,thus posing a great challenge to the management of the power system.For this reason,exploring an effective prediction tool is crucial for smart grid energy integration,control and operation.In this study,a data-driven PV energy pre-diction model for smart grids was proposed,which accurately predicts future PV power generation through multiple neural networks and historical PV generation data.Multiple prediction algorithms,including Long Short-Term Memory Network(LSTM),Feed-Forward Neu-ral Network(FFNN),and Gated Recurrent Unit(GRU),were compared in the study,and the prediction effectiveness of the algorithms was comprehensively evaluated based on key metrics,such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Co-efficient of Determination(R2).The experimental results showed that LSTM and GRU performed better in dealing with complex temporal dependencies,while FFNN was more effective in predicting a specific number of units(such as100 and 150).The experiments verified the effectiveness and application potential of the proposed method in the field of PV energy prediction,which not only provides a scien-tific basis for the optimization of smart grids,but also provides a powerful decision-making tool for power system managers.
photovoltaic energy predictionneural networkdata-drivenenergy management
万方数据
维普
