Cite
An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings.
MLA
Li, Kehua, et al. “An Agglomerative Hierarchical Clustering-Based Strategy Using Shared Nearest Neighbours and Multiple Dissimilarity Measures to Identify Typical Daily Electricity Usage Profiles of University Library Buildings.” Energy, vol. 174, May 2019, pp. 735–48. EBSCOhost, https://doi.org/10.1016/j.energy.2019.03.003.
APA
Li, K., Yang, R. J., Robinson, D., Ma, J., & Ma, Z. (2019). An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. Energy, 174, 735–748. https://doi.org/10.1016/j.energy.2019.03.003
Chicago
Li, Kehua, Rebecca Jing Yang, Duane Robinson, Jun Ma, and Zhenjun Ma. 2019. “An Agglomerative Hierarchical Clustering-Based Strategy Using Shared Nearest Neighbours and Multiple Dissimilarity Measures to Identify Typical Daily Electricity Usage Profiles of University Library Buildings.” Energy 174 (May): 735–48. doi:10.1016/j.energy.2019.03.003.