Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
By Pınar Tüfekci. 2014
457 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/f164ee353ada936ef97d0d379f8581336ddc9733
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Shashank Rajput
- Lifeng Lai
- M. Gamal
- Giles Hooker
- Dimitris Papailiopoulos
- Tuan Dinh
- Yuchen Zeng
- Kangwook Lee
In Electrical and Electronic Engineering
- Shashank Rajput
- Lifeng Lai
- M. Gamal
- Giles Hooker
- Dimitris Papailiopoulos
- Tuan Dinh
- Yuchen Zeng
- Kangwook Lee
Citations in the corpus
- Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution (2018, background): “Results on four real world data sets selected from UCI Machine Learning Repository (Dheeru and Karra Taniskidou, 2017; Tüfekci, 2014; Kaya et al., 2012) are shown in Figure 3.”
- Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation (2014, background): “, 2001) and UCI benchmark datasets (Catlett, 1991; Kaya et al., 2012; Lucas et al., 2013; Tüfekci, 2014; Lichman, 2013).”
- On randomized distributed coordinate descent with quantized updates (2016, methodology): “The data set we use is collected from a power plant [11].”
- LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022, methodology): “We also use four real datasets: Medical Insurance (Insurance) [41], Combined Cycle Power Plant (CCPP) [42], Servo [43], and Student Performance (Student) [44].”