Kaya, HeysemTüfekçi, PınarUzun, Erdinc2022-05-112022-05-1120191300-06321303-6203https://doi.org/10.3906/elk-1807-87https://hdl.handle.net/20.500.11776/6124Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of costly continuous emission monitoring systems used in gas-turbine-based power plants. Their implementation relies on the availability of appropriate and ecologically valid data. In this paper, we introduce a novel PEMS dataset collected over five years from a gas turbine for the predictive modeling of the CO and NOx emissions. We analyze the data using a recent machine learning paradigm, and present useful insights about emission predictions. Furthermore, we present a benchmark experimental procedure for comparability of future works on the data.en10.3906/elk-1807-87info:eu-repo/semantics/openAccessPredictive emission monitoring systemsCONOxexhaust emission predictiongas turbinesextreme learning machinedatabaseExtreme Learning-MachineModelArticle27647834796Q4WOS:0005061654000542-s2.0-85076639070Q3