基于时间序列的电能表月故障数预测方法

针对当前国网信息系统中电能表故障预测模型比较简单、不够全面和没有具体电能表月故障数预测模型的问题，基于时间序列建立综合时间序列预测模型，实现对批次电能表月故障数较准确的预测。首先计算电能表月故障数的移动平均序列，去除微小波动；然后根据序列是否有明显长期趋势，选用ARIMA模型或指数平滑模型对移动平均序列进行预测；最后采用反向移动平均，实现对整个批次电能表月故障数准确的短期预测。通过与BP神经网络模型的预测进行对比，验证了综合时间序列模型的实用性和准确性。在此基础上，建立电能表月故障总数预测模型。计量资产管理部门可以根据所提方法对故障电能表数进行预测，根据预测结果进行备货，提高管理部门的资源配置合理性和工作效率。

A New Method for Predicting the Monthly Fault Number of Watt-hour Meters Based on Time Series

The existing watt-hour meter fault prediction models in the State Grid information system are relatively simple and insufficient, and there is no specific model for predicting the monthly fault number of watt-hour meters. Based on time series, an integrated time series prediction model is established for an accurate prediction of the monthly fault number of batch watt-hour meters. Firstly, the moving average sequence is calculated for the monthly fault number of watt-hour meters to remove small fluctuations. And then, the ARIMA model or exponential smoothing model is selected to predict the moving average sequence according to the long-term trend of the sequence. Finally, the reverse moving average is used to realize the accurate short-term prediction of the monthly fault number of the whole batch of watt-hour meters. By comparison with the BP neural network model, the practicability and accuracy of the proposed time series model is verified. On this basis, a monthly fault prediction model is established. The measurement asset management departments can use the proposed method to predict the number of faulted watt-hour meters, and prepare the stock according to the prediction results, consequently improving the rationality of resource allocation and work efficiency.