Average reward adjusted deep reinforcement learning for order release planning in manufacturing

Schneckenreither, Manuel; Haeussler, Stefan; Peiró, Juanjo

doi:10.1016/j.knosys.2022.108765

Cited by 10 publications

(1 citation statement)

References 49 publications

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“…On the basis of these findings, Mahesh, (2018) proposes that in reinforcement learning, a software agent determines the ideal behaviour in a specific context for a particular problem. This view is supported by Schneckenreither, Haeussler and Peiró, (2022) who writes that the agent takes the input and decides the best action for the problem and then based on the result of the action the agent then receives simple reward feedback to allow it to learn from its behaviour. In unsupervised learning the outputs (labels) aren't known, as the models find patterns and structure from the data without any assistance (Greener et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics

Egwim

Alaka

Egunjobi

et al. 2022

JEDT

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Purpose This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings. Design/methodology/approach This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics. Findings Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting. Research limitations/implications While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK. Practical implications This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system. Originality/value This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

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Section: Literature Reviewmentioning

confidence: 99%