对几个模型进行比较评估,以预测钢厂每小时的电力使用量
1School of Economics and Management, Panzhihua University, 10 Jichang Road, East District, Panzhihua, Sichuan, People's Republic of China.
Scientific reports
|March 12, 2026
概括
精确预测钢铁厂的电力消耗对于节省成本和提高效率至关重要. 双向LSTM模型提供最精确的每小时预测,性能优于XGBoost和工业能源管理的标准LSTM.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 能源管理 能源管理
背景情况:
- 钢铁行业的电力密集度很高,这使得电力成本成为相当大的运营开支.
- 准确的短期电力消耗预测对于优化生产,管理需求费用和实现可持续运营至关重要.
研究的目的:
- 为了比较 eXtreme Gradient Boosting (XGBoost),Long Short-Term Memory (LSTM) 和双向LSTM (BiLSTM) 在钢铁厂每小时功率负载预测中的有效性.
- 使用RMSE,MAE和MAPE等指标来评估模型性能.
主要方法:
- 利用一个集成钢铁厂一年的每小时运行数据 (8,760条记录).
- 实施了预处理管道,包括异常值检测,插值和时间数据分割.
- 通过使用24小时滑窗方法训练和评估XGBoost,LSTM和BiLSTM模型.
主要成果:
- 所有模型都捕获了周期性负载变化;BiLSTM实现了最高准确度,绝对和相对误差最低.
- XGBoost表现出竞争力的表现和可靠的趋势预测.
- 单向LSTM模型的相对误差较高,特别是在电力需求较低的时期.
结论:
- 像BiLSTM这样的双向循环结构对于钢铁制造业的短期电力消耗预测更优越.
- 该研究为在工业能源管理实践中选择合适的模型提供了宝贵的见解.
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