About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Energy Technologies and CO2 Management: Resource Efficient Processes
|
| Presentation Title |
The Optimization Analysis of the Electricity Demand Forecasting Model within Steel Enterprises |
| Author(s) |
Minghui Chi, Yingqin Wang, Zhongheng Chen, Xiancong Zhao, Yafei Wu, Wei Li, Hao Bai |
| On-Site Speaker (Planned) |
Yingqin Wang |
| Abstract Scope |
The steel making process exhibits considerable complexity, with energy consumption accounting for 20%–40% of total production cost. Within this context, electricity represents approximately 10% of total energy cost and serves as both primary and secondary energy sources for steel production. Steel plants have many power supply and demand units, and their electricity needs are affected by various factors on both the supply and demand sides. If supply and demand don’t match, it can cause power surges, damage equipment, and lead to costly production delays. This study proposes a power demand forecasting model for steel plants, analyzing key factors affecting supply and demand. By combining production data and gas usage patterns, the model predicts short-term power demand, helping optimize electricity pricing. At the same time, the implementation of time-of-use power price mechanisms optimizes production scheduling, providing grid operators with actionable load-shifting recommendations. This combined strategy improves cost-effectiveness for local power grids. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Other, Other, Other |