基于传统CNN-LSTM模型和PGAN模型的用电量预测对比研究

为保证新一代智能电网能够根据实时的用电量情况动态的调节区域内电能分配及调度，需要实现高效且精准的用电量预测。传统电网中用电量预测方法是通过人工统计或者对历史同期用电量分析，粗略的计算出可能产生的用电量，不但消耗大量的人力物力，且无法满足智能电网背景下的用电量精准预测。现在采用差分整合移动平均自回归预测模型，长短时记忆网络预测模型和生成对抗网络预测模型等方法对用电量预测问题进行了研究，以取代传统的用电量预测方法。结果表明，智能算法可以大大程度上提高用电量预测的准确性，但要实现短时高效预测，还需在智能电网系统中对智能算法合理使用。

Based on traditional CNN-LSTM model and PGAN model comparative study on power consumption prediction

In order to ensure that the new generation of smart grid can dynamically adjust the regional power distribution and scheduling according to the real-time power consumption, it is necessary to achieve efficient and accurate power consumption prediction. The traditional power consumption prediction method is to calculate the possible power consumption roughly through manual statistics or analysis of the power consumption in the same period of history, which not only consumes a lot of manpower and material resources, but also can not meet the accurate power consumption prediction under the background of smart grid. In order to replace the traditional power consumption forecasting methods, the differential integrated moving average autoregressive forecasting model, long-term memory network prediction model and generative confrontation network prediction model are used to study the power consumption prediction. The results show that the intelligent algorithm can greatly improve the accuracy of power consumption prediction, but to achieve short-term and efficient prediction, it is necessary to use the intelligent algorithm reasonably in the smart grid system.