A power load forecasting system (10) based on a long short-term memory neural (LSTM) network, wherein the LSTM network comprises an input layer, an LSTM network layer, and an output layer. The system comprises: an information receiving module (101) used for transmitting input power load data and region feature factor at a historical moment to the input layer; a modeling module (102) used for training and modeling the power load data and the region feature factor at the historical moment by means of the LSTM network layer, in order to generate a deep neural network load forecasting model; a power forecasting module (103) used for forecasting the power load in a region by means of the deep neural network load forecasting model, and generating a forecasting result of the power load in the region by means of a regressor connected to the LSTM network layer; and a result output module (104) used for outputting the forecasting result of the power load in the region by means of the output layer. By constructing a load forecasting model for multi-task learning on the basis of an LSTM network, power consumption loads in multiple regions can be precisely forecasted, and the forecasting effect is improved.
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