Prediction of Suzhou’s Industrial Power Consumption Based on Grey Model with Seasonal Index Adjustment
Source abstract
Abstract
The accurate prediction of industrial power consumption is conducive to the effective allocation of power resources by power and energy institutions, and it is also of great significance for the construction and planning of the national grid. By analyzing the characteristics of the data of Suzhou’s industrial power consumption between 2003 and 2005, this paper proposes a grey model with a seasonal index adjustment to predict industrial power consumption. The model results are compared with the traditional grey model, as well as the real value of Suzhou’s industrial power consumption, which shows that our model is more suitable for the prediction of industrial power consumption. The lasted Suzhou’s industrial power consumption data, from 2019–2021, are also investigated, and the results show that the prediction results are in very good agreement with the real data. The highlights of the paper are that all precision inspection indexes are excellent and the seasonal fluctuations in the data changes can be reflected in the present model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Grey System Theory Applications · Energy Load and Power Forecasting · Solar Radiation and Photovoltaics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Management Science and Engineering, Guangxi University of Finance and Economics, Nanning 530007, China
School of Artificial Intelligence, Beijing Institute of Economics and Management, Beijing 100102, China
College of Logistics Management and Engineering, Nanning Normal University, Nanning 530001, China
Funding
Guangxi First-class Discipline Statistics Construction Project Fund · Guangxi Philosophy and Social Science Planning Research Project · Training Program for Thousand Young and Middle-aged Key Teachers in Guangxi Universities
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