基于扩展粗糙集的短期电力负荷预测模型

Short-term load forecasting model based on extended rough set

影响短期电力负荷预测的因素众多,如何有效地判断和选择这些相关因素是改善电力负荷预测的关键,通过引入数据挖掘中粗糙集约简算法来解决这一难题。针对常规粗糙集算法计算量大,且不具备容错性和泛化能力,在属性约简过程中设置了分类可信度β,因而对数据具有了一定的容错性和泛化能力,增强了抗噪声能力。经过对实际数据的计算分析,证实了本文提出的方法在一定程度上提高了负荷预测的精度和速度。

There are many factors that influence short-term load forecasting（STLF）, how to justify and select the correlative factors is the key to improve the performance of load forecasting. A reduction algorithm based on rough set theory is proposed to mine more correlative attributes in the pending forecasting components. A reduction algorithm through classification reliability algorithm which with certain noise and having very good cover ability and generalizable ability through set classification reliability-β is introduced to overcome the large computational complexity of conventional reduction algorithm.