Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

• Published in: Applied energy Vol. 160; no. C; pp. 153 - 163
• Main Author: Hsu, David
• Format: Journal Article
• Language: English
• Published: United Kingdom Elsevier Ltd 15.12.2015; Elsevier
• Subjects: algorithms; Buildings; Cluster stability; Cluster-wise regression; data collection; energy; ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Energy consumption; Latent class regression; MATHEMATICS AND COMPUTING; New York; prediction; Prediction accuracy; regression analysis; New York; Prediction accuracy; Cluster-wise regression; Energy consumption; Cluster stability; Latent class regression; Buildings

•Summary of recent work identifying sub-groups of energy consumption in buildings.•Clusterwise (or latent class) regression gives superior prediction accuracy.•K-means gives more stable clusters when the correct number of clusters is chosen.•A tradeoff between prediction accuracy and cluster stabili...