基于并联时序卷积神经网络的电力负荷短期预测

Short-term power load forecasting based on parallel sequential convolutional neural network

中文关键词: 电力负荷  短期预测  并联时序卷积  神经网络  训练模型  周期特征

英文关键词: power load  short-term forecasting  parallel sequential concolutional  neural network  training model  cycle characteristics

中文摘要:

      电力负荷的短期预测可以合理地确定机组运行方式,安排日调度计划,提高计量精准度,对实现系统功率平衡和保障系统的安全经济运行具有重要意义。在利用神经网络进行负荷短期预测时,若训练数据不足,模型的学习能力会大大降低。同时,由于电力负荷数据存在小时周期、日周期、周周期、季节周期特性,常规神经网络训练模型无法反映负荷的不同周期特性,对预测结果的准确性也会造成一定的影响。为此,首先提出一种数据增强方法,有效解决电力负荷预测中训练数据不足的问题。其次,针对负荷的周期性特征,进一步提出使用不同周期特征的并联时序卷积神经网络模型的融合方案,有效反映负荷数据的多周期特征,从而提升电力负荷短期预测的准确度。通过对某地市不同电压等级线路负荷数据的建模和训练,验证所提方法在短期预测方面的有效性和优越性。

英文摘要:

      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.