Envisaging Industrial Perspective Demand Response Using Machine Learning-Reference-Cited by-同舟云学术

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Envisaging Industrial Perspective Demand Response Using Machine Learning

Published:2022 Issue: Volume: Page:331-342
ISSN:2367-4512
Container-title:Proceedings of Data Analytics and Management
language:
Short-container-title:

Author:

Hasan Nabeela,Alam Mansaf

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-6289-8_28

Reference36 articles.

1. Antonopoulos I, Robu V, Couraud B, Kirli D, Norbu S, Kiprakis A, Flynn D, Elizondo-Gonzalez S, Wattam S (2020) Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review. Renew Sustain Energy Rev 130:109899. ISSN 1364-0321 https://doi.org/10.1016/j.rser.2020.109899

2. Chamoso P, Rivas A, Martín-Limorti JJ, Rodríguez S (2018) A hash based image matching algorithm for social networks. In: Advances in intelligent systems and computing, vol 619, pp 183–190

3. Vázquez-Canteli JR, Nagy Z (2019) Reinforcement learning for demand response: a review of algorithms and modeling techniques. Appl Energy 235:1072–1089. ISSN 0306-2619. https://doi.org/10.1016/j.apenergy.2018.11.002

4. Krč R, Kratochvílová M, Podroužek J, Apeltauer T, Stupka V, Pitner T (2021) Machine learning-based node characterization for smart grid demand response flexibility assessment. Sustainability 13(5):2954. https://doi.org/10.3390/su13052954

5. Sittón I, Rodríguez S (2017) Pattern extraction for the design of predictive models in industry 4.0. In: International conference on practical applications of agents and multi-agent systems. Springer, Cham, pp 258–261

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using Supervised Learning to Assign New Consumers to Demand Response Programs According to the Context;2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe);2022-06-28

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