When Heating and Cooling Objectives Pull a Design Apart
Abstract
The question concerns cooling minus heating load as a distinct multi-outcome comparison. The secondary ordinary least squares analysis contains 768 observations and uses HC3 covariance. The coefficient on X5 is 0.1296, with a 95 percent interval from -0.1935 to 0.4527. Cooling minus heating makes disagreement between outcomes visible rather than forcing both into a single arbitrary score. The sign of a coefficient describes a conditional change in that gap, not a universally preferred building design. Adoption and avoidance groups depend on which load consequence matters, and a multi-objective comparison must declare that preference separately from estimation. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
H. Xiao, S. Gao, and L. H. Lee, “Simulation Budget Allocation for Simultaneously Selecting the Best and Worst Subsets,” Automatica, vol. 84, pp. 117–127, 2017.
W. Liu, S. Gao, and L. H. Lee, “A Multi-Objective Perspective on Robust Ranking and Selection,” Proceedings of the 2017 Winter Simulation Conference, pp. 2116–2127, 2017.
Athanasios Tsanas, Angeliki Xifara. Energy Efficiency. UCI Machine Learning Repository, 2012. Dataset. https://doi.org/10.24432/C51307.
White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.