基于多重影响因素的神经网络中短期用电量预测模型
A Neural Network Mid-Term and Short-Term Electricity Consumption Forecast Model Based on Multiple Influencing Factors

当前，电网规划建设由满足电力负荷平衡需求到电量平衡需求转变。新形势下需要探索和拓展考虑多重影响的因素的电量预测方法。本文在电量影响因素分析基础上，采用BP神经网络、RBF神经网络两种不同的预测模型和方法，将用电量和主要影响因素作为输入条件，构建、训练电量预测模型，最后以某区域电量为实例，验证模型方法的准确性和实用性，助力电力负荷平衡向电量平衡转变。
At present, the planning and construction of power grids have shifted from satisfying the demand for electric load balancing to the demand for electric power balancing. Under the new situation, it is necessary to explore and expand electricity forecasting methods that consider multiple influencing factors. Based on the analysis of power influencing factors, this paper adopts two different prediction models and methods of BP neural network and RBF neural network. The power consumption and main influencing factors are used as input conditions to construct and train the power prediction model. Finally, the power consumption in a certain area As an example, it verifies the accuracy and practicability of the model method, and facilitates the transition from power load balance to power balance.