基于事例推理短期负荷预测方法的改进

针对基于事例推理（CBR）短期负荷预测中的事例库组织，提出第一级按不同的时刻和星期类型粗分类、第二级按照模糊聚类方法细分类的二级分类方法，可以很好地实现不同预测环境之间的相似性和相异性；针对事例的检索，提出模糊优先比的定量属性检索方法，按此方法进行检索不但可以提高检索效率，还可以对检索过程进行控制．实际算例表明，以此方法进行负荷预测的周平均相对误差为2．620%，低于一般的CBR方法和单一预测方法．

Improved Short-Term Load Forecasting Based on Case-Based Reasoning

Aiming on the case-base organization in short-term load forecasting with case-based reasoning, a classifying method is presented which briefly classifies the data according to the distinguished time intervals and weekdays, and then schemes the environments by fuzzy clustering to reflect the similarity and differences among the forecasting environments. For case retrieval, a new quantitative strategy based on fuzzy sets is then proposed to improve the efficiency and govern the total procedure. The practical example shows that the proposed CBR method outperforms normal CBR method and the other competing methods for forecasting accuracy.