A steel industry power demand prediction method and system based on multiple source factors

CN121615872BActive Publication Date: 2026-06-09NORTH CHINA GRID MEASUREMENT CENT +2

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner: NORTH CHINA GRID MEASUREMENT CENT
Filing Date: 2025-12-15
Publication Date: 2026-06-09

AI Technical Summary

Technical Problem

Existing methods for forecasting electricity demand in the steel industry based on multiple factors fail to effectively perform spatiotemporal alignment and preprocessing, resulting in data bias, distorted correlation analysis, failure to fully identify core coupling factors, and inability to capture dynamic influence patterns.

Method used

Multi-source data from the target area is collected, spatiotemporal alignment and data preprocessing are performed, the data is divided into multi-source factor standard sub-data, correlation analysis is conducted, electricity consumption correlation factors are identified, an electricity consumption prediction model is constructed, and data is collected in real time for prediction.

Benefits of technology

It has achieved standardized integration of multi-source heterogeneous data, accurately identified factors affecting electricity demand, improved the pertinence and effectiveness of correlation analysis, realized highly accurate dynamic forecasting of electricity demand, and solved the problems of lag and poor adaptability of traditional forecasting.

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Abstract

The application discloses a steel industry power demand prediction method and system based on multiple source factors, relates to the technical field of data processing and big data analysis, and comprises the following steps: collecting historical environment, port throughput, logistics, power demand and steel industry chain related data of a target region, performing space-time alignment, preprocessing and monthly granularity division to obtain multiple source factor standard sub-data; screening first power consumption related factors by correlation analysis and trend chart construction, determining power consumption related coupling factor pairs and short-term accidental, long-term stable related dynamic factors; constructing a prediction model based on time function and periodic function fitting, collecting data in real time, combining sudden factors such as extreme weather and policy adjustment for correction, and outputting steel industry power demand prediction values. The application has the advantages that multiple source factors are deeply fused, the prediction is accurate and reliable, and effective support can be provided for power grid load management and steel industry production planning.

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