Short-Term Telephone-Traffic Prediction of Power Grid Customer Service Based on Adaboost-CNN

doi:10.16183/j.cnki.jsjtu.2023.383

Abstract

Abstract:

The introduction of modern power supply service system has raised higher requirements for the service quality of electricity customer service. Accurate power supply service traffic prediction not only improves the quality of power customer service, but also effectively reduces the cost of customer service personnel. Therefore, this paper proposes a short-term traffic prediction method for power grid based on Adaboost and convolutional neural network (Adaboost-CNN) and a value-added service correction method. First, the isolated forest algorithm is used to identify the abnormal data, and the Lagrange interpolation function is applied to repair the abnormal data or missing data. Next, the analytic hierarchy process is employed to quantify user information, meteorological data, and power outage details. The grey correlation method is then used to analyze the influence factors of traffic volume, and these factors are incorporated as inputs to the traffic volume prediction model. An Adaboost algorithm is applied to integrate multiple CNN models, resulting in an Adaboost-CNN traffic prediction model. Finally, considering the value-added services within the power supply service system, the prediction results of the model are corrected to obtain the final traffic prediction value. The case analysis shows that the proposed forecasting model reduces prediction error by an average of 11.05 percentage points compared to a single forecasting model and by 5.32 percentage points compared to a combined forecasting model, demonstrating better forecasting accuracy.

Key words: modern power supply service system, traffic prediction, Adaboost, convolutional neural network (CNN), isolated forest algorithm, value-added services

CLC Number:

QIN Hao, SU Liwei, WU Guangbin, JIANG Chongying, XU Zhipeng, KANG Feng, TAN Huochao, ZHANG Yongjun. Short-Term Telephone-Traffic Prediction of Power Grid Customer Service Based on Adaboost-CNN[J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 266-273.

Figures/Tables 7

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影响因素	量化值	影响因素	量化值
工作日	1	雷电橙色预警	3
周末	2	雷电红色预警	5
其他节假日	3	晴天	1
不停电	0	多云	2
计划检修停电	2	阴天	3
故障停电	4	小雨	4
无雷电	0	中雨	5
雷电蓝色预警	1	大雨	6
雷电黄预警	2	雪天	7

方法	异常数据识别准确率/%	方法	异常数据修正准确率/%
标准差法	85.6	平均值法	76.3
箱线图法	88.5	多重填补法	84.7
聚类法	93.3	线性插值	88.6
本文方法	100.0	本文方法	91.2

影响因素	相关性	是否为影响因子
最高温	0.643 1	是
最低温	0.447 2	否
天气	0.328 7	否
雷电	0.578 9	是
停电	0.863 4	是
风级	0.214 5	否
节假日	0.572 3	是

模型	e_MAE/通	e_MAPE/%
ARIMA	2 224.1	32.78
ELM	1 792.6	26.42
LSTM	1 380.1	19.34
Adaboost	995.4	14.67
CNN	1 302.0	15.21
单一预测模型平均误差	1 538.8	21.68
LSTM-CNN	1 176.9	17.35
EMD-CNN	974.6	14.37
组合预测模型平均误差	1 075.6	15.86
所提方法(不考虑增值服务)	850.8	12.54
所提方法(考虑增值服务)	721.2	10.63