Towards an efficient and interpretable Machine Learning approach for Energy Prediction in Industrial Buildings: A case study in the Steel Industry

Chahbi, Ismehene; Rabah, Nourhene Ben; Tekaya, Ines Ben

doi:10.1109/aiccsa56895.2022.10017816

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Carlson et al [170,187] used PI to increase the interpretability of an ANN-based electricity-load prediction. Chahbi et al [188] used both RF and PI models for the evaluation of building energy consumption. RF was used for the predictions, and PI was used for to make the outcomes interpretable.…”

Section: Other Techniquesmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

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Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

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Section: Other Techniquesmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

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A Bayesian Optimization Approach of Ensemble and Decision Tree Learning Applied to Industrial Energy Consumption Prediction

De A. Fernandes,

Seppe,

Júnior

et al. 2023

2023 15th IEEE International Conference on Industry Applications (INDUSCON)

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Contributions of the 5G Network with Respect to Decent Work and Economic Growth (Sustainable Development Goal 8): A Systematic Review of the Literature

Beltozar-Clemente,

Iparraguirre-Villanueva,

Pucuhuayla-Revatta

et al. 2023

Sustainability

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Decent work and economic growth are fundamental elements for the sustainable development of a society, with Sustainable Development Goal 8 (SDG8) being one of the key objectives of the United Nations’ 2030 Agenda. The 5G network has great potential to contribute significantly to the achievement of SDG8, offering faster and more reliable connectivity, which opens up new possibilities for innovation, operational efficiency, and job creation. The present study aimed to investigate the role of 5G technologies concerning decent work and economic growth (SDG8). As part of the method, 265 articles extracted from main databases such as Scopus, IEEExplore, and ScienceDirect were analyzed using the PRISMA methodology, resulting in 74 relevant articles after applying the inclusion and exclusion criteria. As a result, a greater contribution to the use of the 5G network was identified in sectors such as manufacturing, health, and transportation, generating greater economic growth and job creation. It was also found that the technological applications with the greatest contributions are “Internet of Things” and “Artificial intelligence”. Finally, it was concluded that the results of this review are useful for future research on technologies that support 5G networks, contributing to economic growth and equitable and sustainable decent work in a wide range of sectors and rural areas.

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Towards an efficient and interpretable Machine Learning approach for Energy Prediction in Industrial Buildings: A case study in the Steel Industry

Cited by 3 publications

References 24 publications

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

A Bayesian Optimization Approach of Ensemble and Decision Tree Learning Applied to Industrial Energy Consumption Prediction

Contributions of the 5G Network with Respect to Decent Work and Economic Growth (Sustainable Development Goal 8): A Systematic Review of the Literature

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