基于季节性指数平滑法的电能表需求预测分析

随着智能电能表大规模普及应用,电能表需求数量日益庞大,采用经验人工估计容易造成电能表临时短缺和库存积压等问题。基于计量系统中的电能表历史安装数据,采用简单季节性模型、Winters加法模型和Winters乘法模型,并结合LSTM模型对结果进行对比分析,比较不同模型的优劣,确定电能表需求预测最优方法。实证结果表明：Winters乘法模型预测效果最好,预测值平均误差为2.46%,预测趋势符合实际情况。Winters乘法指数平滑法能够对电能表需求变化趋势进行科学合理预测,可以协助供电公司更加高效开展电能表运营管理,提高计量资产利用效率。

Demand forecasting analysis of electric energy meter based on seasonal exponential smoothing

With the widespread application of intelligent energy meters, the demand for energy meters is becoming increasingly large. Using empirical manual estimation can easily lead to temporary shortages and inventory backlog of energy meters. Based on the historical installation data of electricity meters in the metering system, simple seasonal model, Winters addition model, and Winters multiplication model are used. And LSTM model is combined to compare and analyze the results, compare the advantages and disadvantages of different models, and determine the optimal method for predicting electricity meter demand. The empirical results indicate that the Winters multiplication model has the best prediction effect, with an average error of 0.96% in the predicted values, and the predicted trend is in line with the actual situation. Winters multiplication index smoothing method can scientifically and reasonably predict the trend of electricity meter demand changes, assist power supply companies in more efficient operation and management of electricity meters, and improve the efficiency of measuring asset utilization.