空间负荷预测中确定元胞负荷合理最大值的主成分分析法

A PCA method for ascertaining maximal value of cellular load in spatial load forecasting

针对若直接使用元胞负荷实测数据中的年最大值进行城市电网空间负荷预测,则极有可能将测量、通信等误差造成的随机波动带入预测结果,而导致预测精度降低的问题,提出了利用主成分分析技术确定元胞负荷合理最大值的方法。该方法通过分析元胞负荷历史数据,利用主成分分析法将元胞负荷分解为表征元胞负荷总体信息的主成分分量和刻画随机波动的非主成分分量。通过剔除非主成分分量来抑制随机波动带来的不利影响,提取出主成分分量中的最大值作为元胞负荷合理最大值,并使用该最大值进行空间负荷预测。实例分析表明,该方法是有效的。

If the annual maximum value of measured data of cellular load is used directly to forecast the spatial load of urban power grids, errors caused by measurement, random fluctuations and communication will be probably intro- duced to the predicted results, which led to reduce prediction accuracy. A method for ascertaining maximal value of cellular load in spatial load forecasting based on PCA is proposed. By analyzing the historical data of cellular load and using PCA, the cellular load is decomposed into the principal components which can characterize cellular load general information and non-principal components which can characterize cellular load random fluctuations. By excluding non- principal components to inhibit the adverse effects caused by random fluctuations, and extract the principal compo- nents of cellular load as the reasonable maximum value of cellular load, and it is used for spatial load forecasting. Case study shows that the method is effective.