  Power plant is a complex and nonstationary system for which the traditional
machine learning modeling approaches fall short of expectations. The
ensemble-based online learning methods provide an effective way to continuously
learn from the dynamic environment and autonomously update models to respond to
environmental changes. This paper proposes such an online ensemble regression
approach to model power plant performance, which is critically important for
operation optimization. The experimental results on both simulated and real
data show that the proposed method can achieve performance with less than 1%
mean average percentage error, which meets the general expectations in field
operations.
