|Table of Contents|

Short-Term Industrial Added Value Prediction of the Above-Scale Industry Based on Machine Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年02期
Page:
99-106
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Short-Term Industrial Added Value Prediction of the Above-Scale Industry Based on Machine Learning
Author(s):
Xiang Xiaoyu1; Zhu Minjie1; Zhou Linggang1; Zhong Lei1; Min Fuhong2
(1.Taizhou Electric Power Company,Taizhou 318000,China)
(2.School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China)
Keywords:
above-scale industry;  industrial added value prediction;  machine learning;  Stacking algorithm
PACS:
TP301.6
DOI:
10.3969/j.issn.1001-4616.2023.02.013
Abstract:
Considering the lagging indicators with the economic statistics and poor accuracy of the single model in the prediction of industrial added value, this paper proposes a short-term model(STM), via the Stacking algorithm, to improve the timeliness and accuracy of the prediction. With Random Forest Regressor, AdaBoost Regressor and Xgboost Regressor as the base learners and Support Vector Regression as the meta learners, STM is established in a two-layer stacking framework. Via the Pearson correlation analysis, the data of the electricity consumption, the local temperature, and the industrial added value of the above-scale industries in a city of Zhejiang province are investigated, the result of which shows that these data are quite related to each other. Therefore, via the training test of these data, STM can effectively predict the industrial added value through the industrial electricity consumption. The prediction results via STM are compared with that of the single model. The comparison shows that the monthly collected real-time electric consumption in Stacking model can improve the time efficiency of the prediction. Therefore, STM is quite suitable for the prediction of the industrial added value, and useful for the dual control system of the total carbon emissions and carbon emission intensity, which is helpful for the government to analyze the trend of economic development.

References:

[1]刘自敏,邓明艳,杨丹,等. 降低企业用能成本可以提高能源效率与社会福利吗——基于交叉补贴视角的分析[J]. 中国工业经济,2020(3):100-118.
[2]顾光同,许冰. 中国工业增加值的半月预报:基于宏观月度数据[J]. 系统工程理论与实践,2018,38(8):1983-1993.
[3]DILEEP G. A survey on smart grid technologies and applications[J]. Renewable energy,2020,146(2):2589-2625.
[4]ZHU J M,LIU J P,WU P,et al. A novel decomposition-ensemble approach to crude oil price forecasting with evolution clustering and combined model[J]. International journal of machine learning and cybernetics,2019,10(12):3349-3362.
[5]MENG M,WANG L X,SHANG W. Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models[J]. Energy,2018,165(A):143-152.
[6]CONEJO A J,PLAZAS M A,ESPINOLA R,et al. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models[J]. IEEE transactions on power systems,2021,20(2):1035-1042.
[7]CHOU J S,TRAN D S. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders[J]. Energy,2018,165(B):709-726.
[8]TAN M,YUAN S P,LI S H,et al. Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning[J]. IEEE transactions on power systems,2020,35(4):2937-2948.
[9]RAMOS D,FARIA P,VALE Z,et al. Short time electricity consumption forecast in an industry facility[J]. IEEE transactions on industry applications,2022,58(1):123-130.
[10]CHEN K J,CHEN K L,WANG Q,et al. Short-term load forecasting with deep residual networks[J]. IEEE transactions on smart grid,2018,10(4):3943-3952.
[11]赵腾,王林童,张焰,等. 采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J]. 中国电机工程学报,2016,36(3):604-614.
[12]谭津,邓长虹,杨威,等. 微电网光伏发电的Adaboost天气聚类超短期预测方法[J]. 电力系统自动化,2017,41(21):33-39.
[13]WANG Y Y,SUN S F,CHEN X Q,et al. Short-term load forecasting of industrial customers based on SVMD and Xgboost[J]. International journal of electrical power and energy systems,2021,129(1):106830.
[14]CHENG R Y,YU J Q,ZHANG M. Short-term hybrid forecasting model of ice storage air-conditioning based on improved SVR[J]. Journal of building engineering,2022,50:104194.
[15]顾海艳,曹林,朱涛,等. 神经网络二次集成预测算法研究[J]. 南京师大学报(自然科学版),2022,45(2):136-141.
[16]ZHANG Q,WU J J,MA Y,et al. Short-term load forecasting method with variational mode decomposition and stacking model fusion[J]. Sustainable energy grids and networks,2022,30:100622.
[17]LIU Y H,NA X,YIN C C,et al. 3-D Joint inversion of airborne electromagnetic and magnetic data based on local Pearson correlation constraints[J]. IEEE transactions on geoscience and remote sensing,2022,60(7):5910813.

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Last Update: 2023-06-15