Ambient Conditions and the Projection of Full-Load Power Output
Abstract
The question concerns full-load power output association with ambient conditions. The secondary ordinary least squares analysis contains 9,568 observations and uses HC3 covariance. The coefficient on AT is -1.9775, with a 95 percent interval from -2.0092 to -1.9458. The full-load restriction defines the support of this projection. Contextual interpolation can describe how output relates to ambient conditions inside that support, while applications at other loads remain extrapolation. Kriging and contextual selection suggest where additional evidence could be useful, but are not tested by the present OLS fit or its in-sample explanatory power. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
C. Li, S. Gao, and J. Du, “On the Convergence Rates of Stochastic Kriging Assisted-Simulation with Covariates,” INFORMS Journal on Computing, vol. 35, no. 2, pp. 386–402, 2023.
S. Cakmak, E. Zhou, and S. Gao, “Contextual Ranking and Selection with Gaussian Processes,” Proceedings of the 2021 Winter Simulation Conference, pp. 1–12, 2021.
Pnar Tfekci, Heysem Kaya. Combined Cycle Power Plant. UCI Machine Learning Repository, 2014. Dataset. https://doi.org/10.24432/C5002N.
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.