Equipment Benchmark Prediction Using XGBoost and Bayesian Tuning
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Solution Overview
Problem
Traditional methods for predicting unit equipment benchmark values in power plants face challenges such as low prediction accuracy, complex operations, and inefficiencies due to reliance on manual modeling and machine learning algorithms like data mining and support vector machines, which struggle with large-scale training samples and feature importance determination.
Innovation Solution
A method using the XGBoost algorithm for predicting benchmark values, involving data preprocessing, feature importance calculation via RF out-of-bag estimation, standardization, and Bayesian optimization of XGBoost model parameters to construct a prediction model that improves automation, accuracy, and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual modeling method is used for predicting equipment benchmark values, then the implementation can be performed with basic knowledge and experience, but the prediction accuracy is low and the calculation process is slow
Solution Approach 1:
The patent replaces traditional manual modeling methods with the XGBoost algorithm, substituting human expert knowledge and experience with an automated machine learning system. This substitution enables the model to automatically learn complex non-linear relationships from historical data, significantly improving prediction accuracy while maintaining efficient calculation speed through optimized gradient boosting implementation.
2Quantity of substance
If data mining technology is used for equipment operation benchmark prediction, then the method can handle large datasets, but the fitting is insufficient and logical regression is poor
Solution Approach 1:
The patent employs a composite approach by integrating XGBoost algorithm with feature importance calculation mechanisms. This composite methodology combines the strengths of gradient boosting for handling large datasets with explicit feature selection capabilities, achieving both high data processing capacity and superior fitting accuracy by identifying and weighting the most relevant features.
3Reliability
If support vector machine method is applied to fault warning system, then the classification can be performed, but it is difficult to implement for large-scale training samples
Solution Approach 1:
The patent segments the complex prediction task into manageable components through feature importance calculation and selection. By dividing the feature space and identifying key predictive features, the XGBoost model can efficiently process large-scale training samples without the implementation difficulties associated with support vector machines, maintaining both reliability and computational feasibility.
4Adaptability or versatility
If traditional control means are used for thermal power units with peak load regulation, then the operation can be maintained, but the economic situation deteriorates due to contradiction between strict assessment standards and complex operating conditions
Solution Approach 1:
The patent implements a feedback mechanism by continuously predicting equipment benchmark values based on real-time operating conditions and comparing them with actual performance. This feedback loop enables dynamic optimization of thermal power unit operations, allowing the system to adapt to peak load regulation requirements while maintaining economic efficiency through data-driven decision support for operators.
Data Source
AI summary
The invention relates to a method for predicting benchmark value of unit equipment based on XGBoost algorithm and a system thereof, wherein the method comprises the following steps: the historical operation data of unit equipment is obtained, the data is preprocessed, and a data set containing a plurality of samples is constructed, and each sample includes the benchmark value of a plurality of parameters of the equipment corresponding to a plurality of features; RF out-of-bag estimation is used for feature importance calculation to eliminate the features with low importance; the data is standardized to eliminate the dimensional effects among features; the data set is input to construct an XGBoost model, and Bayesian super parameter optimization is conducted to obtain the prediction model of benchmark values; and the real-time data of equipment operation is input, and the benchmark values of various equipment parameters are predicted by the prediction model of benchmark values. Compared with the prior art, the invention mines the correlation among data based on the XGBoost algorithm to predict a reasonable equipment benchmark value, and has the advantages of high generalization ability, high prediction accuracy and operation speed and great improvement of the automation ability of the unit.