Heating Load across a Released Building Design Space
Abstract
The question concerns heating-load projection on released building design configurations. The secondary ordinary least squares analysis contains 768 observations and uses HC3 covariance. The coefficient on X5 is 5.6068, with a 95 percent interval from 5.0890 to 6.1245. The heating projection is an input to a later design comparison. It can reveal geometry-associated variation, but the selected extreme would require checking on released support and on the intended climate. A smooth contextual surface and a subset allocation have different error targets: local choice and retained membership. Neither is validated here merely by fitting heating output. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
- S. Cakmak, Y. Wang, S. Gao, and E. Zhou, “Contextual Ranking and Selection with Gaussian Processes and Optimal Computing Budget Allocation,” ACM Transactions on Modeling and Computer Simulation, vol. 34, no. 2, pp. 1–24, 2024.
- F. Gao, Z. Shi, S. Gao, and H. Xiao, “Efficient Simulation Budget Allocation for Subset Selection Using Regression Metamodels,” Automatica, vol. 106, pp. 192–200, 2019.
- 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.