With the transformation and upgrading of the power system, the energy supply and consumption of the new power system have undergone a huge change, so the higher demand for the electricity forecast is put forward. The accurate forecast of monthly electric quantity can provide reliable basis for optimal dispatching of new electric power system and marketing plan of electric power market. On the basis of in-depth mining of historical electric quantity data, comprehensive analysis of monthly electric quantity characteristics and the influence of related factors, combined with the advantages of Prophet algorithm and KELM neural network algorithm, a monthly electric quantity combination prediction method considering temperature, economic level and holidays was proposed. Firstly, the Prophet prediction model was established based on monthly electric quantity data, and the parameters were optimized. Secondly, the prediction model based on historical electric quantity, temperature, GDP and holiday information is established by using KELM neural network, and the optimal prediction model is determined by parameter tuning. Finally, the monthly electric quantity combination prediction model is established by means of weighted combination. By example analysis, the prediction error and prediction effect of the combined algorithm are compared with those of other algorithms. It is shown that the combined model proposed in this paper has improved the prediction accuracy, and the validity of the prediction algorithm is verified.