Cite
A data-driven strategy to forecast next-day electricity usage and peak electricity demand of a building portfolio using cluster analysis, Cubist regression models and Particle Swarm Optimization.
MLA
Li, Kehua, et al. “A Data-Driven Strategy to Forecast next-Day Electricity Usage and Peak Electricity Demand of a Building Portfolio Using Cluster Analysis, Cubist Regression Models and Particle Swarm Optimization.” Journal of Cleaner Production, vol. 273, Nov. 2020, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.jclepro.2020.123115.
APA
Li, K., Ma, Z., Robinson, D., Lin, W., & Li, Z. (2020). A data-driven strategy to forecast next-day electricity usage and peak electricity demand of a building portfolio using cluster analysis, Cubist regression models and Particle Swarm Optimization. Journal of Cleaner Production, 273, N.PAG. https://doi.org/10.1016/j.jclepro.2020.123115
Chicago
Li, Kehua, Zhenjun Ma, Duane Robinson, Wenye Lin, and Zhixiong Li. 2020. “A Data-Driven Strategy to Forecast next-Day Electricity Usage and Peak Electricity Demand of a Building Portfolio Using Cluster Analysis, Cubist Regression Models and Particle Swarm Optimization.” Journal of Cleaner Production 273 (November): N.PAG. doi:10.1016/j.jclepro.2020.123115.