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Envisaging Industrial Perspective Demand Response Using Machine Learning

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Proceedings of Data Analytics and Management

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 90))

Abstract

The utilization of Internet of Things (IoT) sensors in industry which generates the data that is used in a variety of analytics for the acquisition of valuable information is characterised as Industrial Internet of Things (IIoT). In predictive analytics, the primary aspect is typically the kind of data provided by the sensors. Various kinds of data are collected while performing predictive modelling, for, e.g., area, season, energy, cost, etc. In this paper, a case study is presented, in which a dataset is used to examine the functioning of equipment and to analyse the demand and response of that equipment. The main aim of this paper is to employ various machine learning algorithms in order to devise of predictive models in industrial IoT environment using the dataset mentioned above.

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Authors and Affiliations

  1. Jamia Millia Islamia, New Delhi, 110025, India

    Nabeela Hasan & Mansaf Alam

Authors
  1. Nabeela Hasan
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  2. Mansaf Alam
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Corresponding author

Correspondence to Nabeela Hasan .

Editor information

Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, New Delhi, India

    Deepak Gupta

  2. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Maharaja Agrasen Institute of Technology, New Delhi, India

    Ashish Khanna

  4. Rajnagar Mahavidyalaya, Birbhum, West Bengal, India

    Siddhartha Bhattacharyya

  5. Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

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Hasan, N., Alam, M. (2022). Envisaging Industrial Perspective Demand Response Using Machine Learning. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_28

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