Research and Application of PV Forecasting and Load Forecasting Algorithm Based on CNN-LSTM Combination Model
The installed photovoltaic capacity in the power grid of oilfield enterprises is growing rapidly,but due to the influence of solar irradiance and other meteorological factors,the intermittency and volatility of photovoltaic power generation have a serious impact on the safe and stable operation of the power grid of oilfield enterprises.At the same time,the previous single model prediction of photovoltaic power genera-tion has certain limitations.Combining the features of convolutional neural network(CNN)and long short-term memory network(LSTM),a combined predic-tion model based on CNN-LSTM was proposed,and simulation experiments were carried out in a photovol-taic power station.The experimental results showed that the root-mean-square error and average absolute error of CNN-LSTM model for PV power prediction were 0.212 1 and 0.129 0,respectively,and the root-mean-square error and average absolute error of load prediction were 0.209 7 and 0.115 5,respec-tively.The validity of the model for power and load prediction of photovoltaic power generation was veri-fied.The predicted results can guide the operation plan of the source network load,improve the photo-voltaic consumption,improve the safe and reliable operation level of the power system,and provide ac-curate decision-making support for the safe and low-carbon operation of the oilfield power grid.
photovoltaic power generationpowerload forecastingmodelsecure
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