# Biogas Production Prediction Based on Feature Selection and Ensemble Learning
###### Abstract  
The allocation of biogas between power generation and heat supply in traditional kitchen waste power generation system is unreasonable; for this reason, a biogas prediction method based on feature selection and heterogeneous model integration learning is proposed for biogas production predictions. Firstly, the working principle of the biogas generation system based on kitchen waste is analyzed, the relationship between system features and biogas production is mined, and the important features are extracted. Secondly, the prediction performance of different individual learner models is comprehensively analyzed, and the training set is divided to reduce the risk of overfitting by combining K-fold cross-validation. Finally, different primary learners and meta learners are selected according to the prediction error and diversity index, and different learners are fused to construct the stacking ensemble learning model with a two-layer structure. The experimental results show that the research method has a higher prediction accuracy in predicting biogas production, which provides supporting data for the economic planning of kitchen waste power generation systems.  
ensemble learning; kitchen waste power generation; biogas prediction; features correlation analysis; K-fold cross-validation +
Footnote †: journal: Medical  
used a combined model approach to fuse the light gradient boosting machine (LightGBM), RF, and SVM algorithms with stacking ensemble models to achieve high recall and accuracy in the classification problem. [PERSON] et al. [11] used a stacking model to fuse multiple XGBoost models and used a two-layer stacking prediction model to maximize the performance of XGBoost, which achieved better results on short-term bus load prediction data, but the prediction results on biogas data were poor, and further analysis of the ensemble model is needed to improve the prediction accuracy.  
Aiming at the problem that existing algorithms are not accurate in predicting biogas production, the biogas power generation system studied in this paper centers on the biogas produced by the fermentation of kitchen waste, and proposes a biogas prediction method from the physical state data of each part of the gas production system. The specific contributions are as follows:  
* We analyze the correlation coefficients between system features and target features, combine the working principle of the system, screen out the features with strong correlation, and obtain the optimal number of input model features based on the preliminary prediction.
* We use the K-fold cross-validation method to divide the dataset proportionally to avoid the overfitting problem.
* A stacking ensemble learning model is constructed based on different models for training and prediction, and the stacking model with the best prediction effect is obtained under different base learners and meta learners.  
The paper is structured as follows: In Section 2, the kitchen waste power system studied in this paper is introduced, and the structure of the proposed biogas prediction model is described in detail. In Section 3, the actual operational data of the kitchen waste system is used for experiments and the results are presented and discussed. In Section 4, the conclusion is presented.