Short-term forecasting of power load can reasonably determine the operation mode of unit

arrange the daily dispatching plan

improve the measurement accuracy

which is of great significance to realize the power balance and ensure the safe and economic operation of the system. When using neural network to predict load in short-term

the learning ability of the model will be greatly reduced if the training data is insufficient. At the same time

due to the characteristics of hourly cycle

daily cycle

weekly cycle and seasonal cycle of power load data

the conventional neural network training model can not reflect the different cycle characteristics of load

which will also have a certain impact on the accuracy of prediction results. Therefore

a data enhancement method is proposed to effectively solve the problem of insufficient training data in power load forecasting. Secondly

according to the periodic characteristics of load

a fusion scheme of parallel sequential convolutional neural network model with different periodic features is furtherly proposed

which effectively reflects the multi-periodic characteristics of load data

and thus improves the accuracy of short- term prediction of power load. Through the modeling and training of load data in a certain city

the effectiveness and superiority of the proposed method in short-term forecasting are verified.