Because of the rapidly increasing total electric load of buildings, effective electric load management should be achieved quickly. This can be realized via electric load forecasting. In this study, a novel clustering-based hybrid prediction model is proposed to predict the 24-daily electric load of buildings. In this study, fuzzy c-means (FCM) clustering, ensemble empirical mode decomposition (EEMD), and some intelligent prediction algorithms are combined. FCM is used to extract the daily data exhibiting similar features, whereas EEMD is used for breaking down the optimal prediction algorithm is selected for each component, and the prediction results are integrated. When compared with the remaining conventional prediction models based on real data, the proposed hybrid model exhibits higher prediction accuracy.

由于建筑物的总用电负荷迅速增加，应迅速实现有效的用电负荷管理。这可以通过电力负荷预测来实现。在这项研究中，提出了一种新的基于聚类的混合预测模型来预测建筑物的 24 天电力负荷。本研究结合了模糊 c 均值 (FCM) 聚类、集成经验模态分解 (EEMD) 和一些智能预测算法。FCM用于提取具有相似特征的日常数据，而EEMD用于分解，为每个组件选择最佳预测算法，并将预测结果整合。与其他基于真实数据的传统预测模型相比，所提出的混合模型具有更高的预测精度。