Abstract: The present disclosure discloses a grid supply load predicting method, a system, and a storage medium. The method includes: determining a characteristic historical data set of a grid supply load; obtaining a grid supply load characteristic trend prediction result by inputting the characteristic data set of the historical grid supply load and the daily characteristic data of target influencing factor into a preset trend prediction model for grid supply load characteristic trend prediction; determining a grid supply load curve type; obtaining a grid supply load prediction model corresponding to the grid supply load curve type to take as a target prediction model; and obtaining a target grid supply load prediction result by inputting the historical data set of the grid supply load, the grid supply load characteristic trend prediction result and the daily characteristic data of target influencing factor into the target prediction model for grid supply load prediction.

Abstract: Disclosed is an optimal power flow acquiring method for regional distribution network of small hydropower groups based on deep learning, which specifically includes the following steps: generating required data sets by adopting continuous power flow and power flow equation calculation methods; the data set is randomly divided into training data (80 percent) and test data (20 percent); training the built convolutional neural network model with training data to learn the mapping relationship between load and generator output power; inputting test data, and directly obtaining PG and QG from the trained convolutional neural network; and solving residual variables Vi and ?i with traditional power flow solver. The application can accelerate the solving speed of the optimal power flow problem with higher prediction accuracy.