A Low-Output Event as a Distinct Feasibility Question
Abstract
The question concerns illustrative below-450-MW event as linear probability. The secondary ordinary least squares analysis contains 9,568 observations and uses HC3 covariance. The coefficient on AT is 0.0334, with a 95 percent interval from 0.0314 to 0.0353. The below-450 event is a declared analytical boundary and not a reliability standard. Its linear probability coefficients are differences on a probability scale under the chosen controls. Joint feasibility in a real application could add requirements absent from this file. Accurate estimation of this one event would not establish those unobserved requirements or a validated selection policy. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
- S. Gao and W. Chen, “Efficient Feasibility Determination with Multiple Performance Measure Constraints,” IEEE Transactions on Automatic Control, vol. 62, no. 1, pp. 113–122, 2017.
- S. Gao and W. Chen, “Feasibility Determination in Presence of Multiple Performance Measure Constraints,” Proceedings of the 2016 IEEE International Conference on Industrial Technology, pp. 988–992, 2016.
- 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.