  The main objective of this paper is to propose K-Nearest-Neighbor (KNN)
algorithm for predicting NOx emissions from natural gas electrical generation
turbines. The process of producing electricity is dynamic and rapidly changing
due to many factors such as weather and electrical grid requirements. Gas
turbine equipment are also a dynamic part of the electricity generation since
the equipment characteristics and thermodynamics behavior change as the
turbines age. Regular maintenance of turbines are also another dynamic part of
the electrical generation process, affecting the performance of equipment. This
analysis discovered using KNN, trained on relatively small dataset produces the
most accurate prediction rates. This statement can be logically explained as
KNN finds the K nearest neighbor to the current input parameters and estimates
a rated average of historically similar observations as prediction.
  This paper incorporates ambient weather conditions, electrical output as well
as turbine performance factors to build a machine learning model to predict NOx
emissions. The model can be used to optimize the operational processes for
reduction in harmful emissions and increasing overall operational efficiency.
Latent algorithms such as Principle Component Algorithms (PCA) have been used
for monitoring the equipment performance behavior change which deeply
influences process paraments and consequently determines NOx emissions. Typical
statistical methods of machine learning performance evaluations such as
multivariate analysis, clustering and residual analysis have been used
throughout the paper.
