Prediction method of power grid marketing cost based on improved ARIMA model

Dian Tong; Song-lin Zhou; Hui Li; Xing Zhang

doi:10.1117/12.2672723

28 April 2023 Prediction method of power grid marketing cost based on improved ARIMA model

Dian Tong, Song-lin Zhou, Hui Li, Xing Zhang

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126103S (2023) https://doi.org/10.1117/12.2672723
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

The current traditional power grid marketing cost forecasting method achieves cost forecasting by studying relevant project examples, which leads to poor forecasting results due to the shallow analysis of cost influencing factors. In this regard, an improved ARIMA model-based power grid marketing cost forecasting method is proposed. The key factors affecting the cost of grid marketing are analyzed by using principal component analysis, and the main indicators affecting the cost are selected by comparing the degree of influence of each indicator on the overall, and the cost prediction model is constructed by using ARIMA algorithm. In experiments, the proposed cost prediction method is validated. The prediction results of the proposed method are in good agreement with the actual cost situation, and the cost prediction performance is better.

Citation Download Citation

Dian Tong, Song-lin Zhou, Hui Li, and Xing Zhang "Prediction method of power grid marketing cost based on improved ARIMA model", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126103S (28 April 2023); https://doi.org/10.1117/12.2672723

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