Power Plant Performance Modeling with Concept Drift
This work addresses power plant operation optimization, but it is incremental as it applies an existing ensemble-based online learning method to a specific domain.
The paper tackled the problem of modeling power plant performance in a nonstationary environment by proposing an online ensemble regression approach, achieving less than 1% mean average percentage error in experiments on simulated and real data.
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.