生物质的一致状态切换Lasso模型高热值近似分析

Akara Kijkarncharoensin, S. Innet
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引用次数: 0

摘要

预测准确性对于推广可再生生物质能源的高热值(HHV)模型至关重要,尤其是在无法获得再培训数据和范围知识的情况下,其一致性至关重要。由于各种原因,目前的HHV模型在准确性和可解释性方面缺乏一致性。因此,本研究旨在在大范围的数据集上构建一个可解释且一致的基于近似的生物质HHV模型。该模型称为状态lasso,融合了状态切换、lasso回归和联合平均的概念,构建了一个一致的HHV模型。制度转换将数据集划分为最优制度,套索训练制度模型。政权套索模型就是这些模型的集合。在文献的大范围数据集中,其均方根误差为0.4430–0.9050,平均绝对误差为0.2743–0.6867,平均绝对错误为1.512–4.5894%。Kruskal–Wallis测试证实,无论训练集如何,样本内的性能一致性为α=0.05。在没有重新训练的样本外情况下,该模型在11个数据集中的6个数据集中保持了其准确性,α=0.01。状态套索的可解释性表明,状态特征是预测不一致的一个因素。FC的增加对第二和第三方案的HHV产生了最大的积极影响,而ASH的增加对第一和第二方案产生了负面影响。VM变异在所有制度中都具有中性效应。状态套索解决了精度下降的问题,并解决了HHV模型灵敏度分析中的挑战。模型直接实现的预测准确性问题得到了解决。
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Consistent Regime-Switching Lasso Model of the Biomass Proximate Analysis Higher Heating Value
Prediction accuracy is crucial for higher heating value (HHV) models to promote renewable biomass energy, especially its consistency is crucial when retraining data and knowledge of the range are unavailable. Current HHV models lack consistency in accuracy and interpretability due to various reasons. Thus, this study aimed to construct an interpretable and consistent proximate-based biomass HHV model on a wide-range dataset. The model, regime-lasso, integrated the concepts of regime-switching, lasso regression, and federated averaging to construct a consistent HHV model. The regime-switching partitioned the dataset into optimal regimes, and the lasso trained the regime models. The regime-lasso model is a collection of these models. It provided root  mean square error of 0.4430– 0.9050, mean absolute error of 0.2743–0.6867, and average absolute error of 1.512–4.5894% in the literature’s wide-range datasets. The Kruskal–Wallis test confirmed the in-sample performance consistency at α=0.05, regardless of the training sets. In the out-of-sample situations without retraining, the model preserved its accuracy in six out of 11 datasets at α = 0.01. The interpretability of regime-lasso indicated the regime characteristic to be a factor of inconsistent prediction. The increase in FC had the maximum positive impact on HHV in the 2nd and 3rd regimes, while the increase in ASH negatively impacted the 1st and 2nd regimes. VM variation had neutral effects in all regimes. The regime-lasso solves the issues of accuracy declination and addresses the challenges in sensitivity analysis of the HHV model. The prediction accuracy issues of the model’s direct implementation were fixed.
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CiteScore
4.50
自引率
16.00%
发文量
83
审稿时长
8 weeks
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