Prediction of Electric Load Neural Network Prediction Model for Big Data


  • Zifan Huang
  • Hongkui Zhong
  • Wenyu Chen
  • Jianhong Wang



Weibull distributed, Apriori algorithm, ARIMA model, Expected utility theory.


Firstly, this article analyzes the distribution patterns and interrelationships of sales volume among different categories of vegetables and individual products. Based on the results of the stability test of the daily sales volume of six types of vegetables, the data of each type of vegetables is divided into 7 sub-data sets according to the week to explore the distribution patterns of vegetable sales volume and time. Subsequently, this article conducts a distribution test on 42 sub-data sets of the six types of vegetables, and finds that the Weibull distribution is the optimal fitting distribution. Spearman correlation coefficients are used to judge the correlation between the six types of vegetables based on scatter plots. The results show that there is weak correlation among different categories of vegetable products. An Apriori algorithm is used to analyze the correlation of 246 individual products, and 24 frequent itemsets are found. Then, a heatmap analysis is conducted on the individual products in each set, which shows strong correlation within the set. Finally, this article divides the daily sales volume of each vegetable category into two time scales, and uses the ARIMA(p,d,q) model and the ARIMA-LSTM model to predict the sales volume for the following week, which facilitates the construction of a sales volume-price model based on future predicted sales volume to predict the price of vegetables for the following week, and further analysis can be conducted.


Download data is not yet available.


Wang Zengfei, Wang Xiaodong, Zhao Anping et al. Market situation and future prospects of Beijing vegetable market in the first half of 2023 [J]. Agricultural Outlook, 2023, 19 (07): 18-23.

Zhang Jing, Liu Jifang, Wu Jianzhai et al. Analysis on operation of vegetable market in 2021 and prospect in 2022 [J]. Chinese Vegetables, 2022 (01): 1-8.

Li Xiaolulu, Zhou Shuguang. Study on development issues of fresh supermarket retail industry in China [J]. Journal of Business Economics, 2021, (23): 35-37.

Ma Xiaoli. Research on inventory management optimization of fresh products in Y chain supermarket [D]. Hebei University of Science and Technology, 2022.

Shen Chen, Zhao Feng, Sun Jiabo et al. Reflections and suggestions on vegetable market regulation in China [J]. Chinese Vegetables, 2022 (10): 14-19.

Gao Yidan, Zong Yixiang. Analysis on vegetable price trend in the first quarter of 2020 in Hebei Province and prediction for the future [J]. Vegetables, 2020 (06): 71-77.

Mao Lisha. Research on pricing strategy and production and marketing model of vegetable wholesale market from the perspective of supply chain [D]. Central South University of Forestry and Technology, 2023.

Tian Dong, Wei Xinhua, Wang Yue et al. Temperature prediction in edible fungi greenhouse based on MA-ARIMA-GASVR[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(03): 190-197.

Cao Xinyue, He Chunlin, and Cui Mengtian. Urban vegetable price fluctuation patterns and prediction based on the X12-ARIMA and LSTM combined model. Journal of Southwest University for Nationalities (Natural Science Edition), 2021, 47(04): 418-425.

Li Yang, Du Ruishan, Cheng Yongchang. Study on crude oil production time series prediction based on ARIMA-LSTM combined model [J]. Mathematics in Practice and Theory, 2022, 52 (06): 40-48.




How to Cite

Huang, Z., Zhong, H., Chen, W., & Wang, J. (2024). Prediction of Electric Load Neural Network Prediction Model for Big Data. Highlights in Science, Engineering and Technology, 82, 339-348.