Combined cycle power plants (CCPP) are among the most sophisticated, yet efficient,
systems for producing electrical energy. Hence, simulating their performance has been an …

Predicting full load electrical power output of a base load power plant is important in order to
maximize the profit from the available megawatt hours. This paper examines and compares …

Density based clustering methods are proposed for clustering spatial databases with noise.
Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover …

A smart city is a sustainable and effective metropolitan hub, that offers its residents high
excellence of life through appropriate resource management. Energy management is …

Predictive emission monitoring systems (PEMS) are important tools for validation and
backing up of costly continuous emission monitoring systems used in gas-turbine-based …

Over the last few decades, various methods have been proposed for estimating prediction
intervals in regression settings, including Bayesian methods, ensemble methods, direct …

This work aims to model the combined cycle power plant (CCPP) using different algorithms.
The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR) …

The high-precision load prediction technology plays a vital role in load control and health
management for gas turbines. Low-calorific fuel fired gas turbines pose an especially …

Influence estimation analyzes how changes to the training data can lead to different model
predictions; this analysis can help us better understand these predictions, the models …

The ability to detect online changes in stationarity or time variance in a data stream is a hot
research topic with striking implications. In this paper, we propose a novel probability density …