Abstract:
In iron and steel enterprises,power load consuming is small. It does not render the pronounced cyclical
variations. Process change leads to instant load fluctuations. The traditional load prediction model cannot effectively predict users’ sudden disturbance. A subspace method for data-driven prediction power load of steel enterprises is used based on subspace algorithm to establish ultra-short term load prediction of power daily load prediction model. To improve the accuracy of prediction models,it introduces the feedback factor and forgetting factor to improve standard subspace algorithm performance. To actual load test data to verify the practicality of the approach method,the results can provide electricity load prediction in steel enterprise and secondary energy smart management provides an effective decision support.
Key words:
data-driven subspace,
feedback factor,
forgetting factor,
wavelet transform,
power load prediction,
intelligent management
摘要： 在钢铁企业中,电力负荷消耗规模相对较小,未呈现明显的周期性变化特征,而工序变化会导致瞬间电力
负荷波动较大,传统负荷预测模型对工业用电预测效果不佳,无法有效预测出用户的突发性扰动。采用数据驱动
的子空间方法预测钢铁企业电力负荷,分别建立基于子空间算法的电力日负荷预测、超短期负荷预测2 个模型。
为提高预测模型准确率,引入反馈因子和遗忘因子来改善标准子空间算法的性能。以实际电力负荷数据的测试验
证该方法的实用性,预测结果能够为钢铁企业的电力负荷预测和二次能源智能管理提供有效的决策支持。
关键词:
数据驱动子空间,
反馈因子,
遗忘因子,
小波变换,
电力负荷预测,
智能管理
CLC Number: