K-PCD: A new clustering algorithm for building energy consumption time series analysis and predicting model accuracy improvement

Abstract

Clustering algorithms are often applied to building energy consumption data analysis to mine representative patterns of building energy usage. This paper proposes a new clustering algorithm: K-PCD, which is particularly suitable for building energy consumption time series. K-PCD utilizes a Pearson correlation coefficient-based distance measure (PCD), and a novel centroid calculation method that takes into account both the PCD and the traditional Euclidean distance (ED) between time series. This study also proposes a new clustering validity index (CVI) tailored to the predictability of building energy consumption: Energy Prediction Clustering Performance Index (EPCPI). The K-PCD and EPCPI are practically analyzed and validated using one year of data from 29 real buildings. The results indicate that comparing with traditional clustering algorithms, the K-PCD achieves better clustering results. This EPCPI index thoroughly explores building operation patterns, and after adding clustering labels, it can maximize the prediction accuracy of the Back Propagation Neural Network (BPNN) model for building energy consumption.