【摘要】 为充分利用智能电表采集的细粒负荷数据并提高区域商业建筑负荷预测的精确度，提出一种基于用电行为模式的区域商业建筑负荷预测方法。首先，基于均值方差归一化方法对采集到的负荷数据进行标准化处理，通过肘方法确定聚类数目后进行k-Shape聚类，实现区域商业建筑负荷不同用电行为模式的提取；其次，针对大规模商业建筑负荷预测问题，考虑区域内大量商业建筑负荷预测时耗费大量内存资源却难以实现较准确预测问题，提出一种改进的Informer模型，该模型通过聚类算法识别具有相似用电行为模式的商业建筑，并充分考虑智能电表采集的异常负荷数据对模型训练结果的影响，能够良好的解决大规模商业建筑负荷预测精度不高问题；最后，采用加利福尼亚州商业建筑负荷进行实验，实验结果表明所提方法能够有效提高区域商业建筑负荷预测精度。更多还原

【Abstract】 A region-based commercial building load forecasting method based on electricity consumption behavior patterns is proposed to fully exploit the fine-grained load data collected by smart meters and to improve the accuracy of regional commercial building load forecasting. Firstly, the mean-variance normalization method is used to standardize the collected load data. Then, to extract different electricity consumption behavior patterns in regional commercial building loads, the elbow method is used to determine the number of clusters, followed by k-Shape clustering. Next, an improved Informer model is introduced to address the challenge of predicting large-scale commercial building loads within a region, which often requires significant memory resources while struggling to achieve high accuracy. This model uses clustering algorithms to identify commercial buildings with similar electricity consumption patterns and accounts for the impact of anomalous load data collected by smart meters on the training results. The proposed model effectively addresses the problem of low accuracy in predicting load for large commercial buildings. Finally, experiments are conducted using commercial building loads in California.The experimental results demonstrate the effectiveness of our proposed method in the improvement of the accuracy of regional commercial building load forecasting.更多还原