Short-Term Load Forecasting Based on SOM-BP and Prophet Hybrid Model

To improve the accuracy of short-term load forecasting in power system, fully exploit the multi-dimensional information in the historical data to better overcome the adverse effects caused by the lack of the historical data, a short-term load forecasting method based on the self- organizing maps and back propagation (abbr. SOM-BP) and Prophet hybrid model was proposed. Firstly, the similar day set was obtained by clustering the historical non-power data through SOM neural network, and then the BP neural network was trained with the similar day data to obtain the single point load value prediction results. Secondly, focusing on the periodicity and temporal trends of historical data, the Prophet temporal model was used to perform periodic nonlinear fitting on historical load data. Through historical data fitting error feedback, the key hyperparameters of the optimization model was adjust, and finally the short-term load forecasting results based on the combination of error reciprocal method were obtained. Finally, Taking the power load data of a certain region as an example for verification, the results show that the proposed improved prediction model has higher prediction accuracy and advantages in overcoming historical data deficiencies and fitting non- working day load curves.