基于主成分分析的用电模式稳定性分析

用电模式稳定性分析是实施用户用电量预测的前提,其本质是考察不同历史时间段用电模式的相似性。过长的基础数据时间跨度会降低用电模式稳定性分析的可行性和准确性,而在短期内基于日用电量数据评估的用电特征指标又受随机因素干扰,难以准确反映用电模式。为此,提出一种以过往几周日用电系数和日用电波动率为原始特征指标提取用电模式主成分,进而用两个历史时间段内用电模式主成分因子载荷的欧氏距离衡量用电模式稳定性的方法。针对某小区的算例结果表明,用所提方法判定为用电模式稳定、不稳定的用户组的用电量预测精度存在明显差异,且相似性距离与预测误差存在正相关性。算例分析表明,合适的历史数据时间跨度对提升方法的适用性和准确性至关重要,所提方法采用16周历史数据较为合理。

Stability Analysis of Consumption Mode Based on Principal Component Analysis

Consumption mode stability analysis(CMSA)is the precondition of load forecasting, the essence of which is to judge the similarity of a customer''s consumption features in different historical time periods. Extracting consumption features from data of a long time period reduces the feasibility and accuracy of CMSA, using the short-time data might also be of low accuracy because daily consumption data are influenced by random factors. A method is therefore proposed to extract the customers'' consumption modes by principal component analysis and taking daily consumption coefficients and daily consumption volatility as original consumption features. The stability of consumption mode is further judged by the Euclidean distance between the factor loading vectors of the principal components in two historical periods. A numerical example is provided by a residential community. Results show that the monthly consumption forecast accuracy is apparently different from the consumers in stable and unstable consumption modes judged by the proposed method and there is significant positive correlation between the similarity distance and the forecast error. It''s also concluded that proper data period is of utmost importance to the feasibility and accuracy of CMSA. It seems 16 weeks will be appropriate for the CMSA problem.