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Power Consumption Optimization for the Industrial Load Plant Using Improved ANFIS-Based Accelerated PSO Technique

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Practical Examples of Energy Optimization Models

Part of the book series: SpringerBriefs in Energy ((BRIEFSENERGY))

Abstract

In recent times, energy saving has become the focus and an interesting topic for engineers and researchers. About 40–44% of the total energy is used for cooling of buildings. As cooling demand increases, the electricity consumption increases proportionally. There are numerous intelligent techniques adopted to evaluate energy usage. This chapter proposes a prediction technique to evaluate energy consumption using improved adaptive neuro-fuzzy inference system (ANFIS) model.

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Author information

Authors and Affiliations

  1. Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Perumal Nallagownden, Elnazeer Ali Hamid Abdalla & Nursyarizal Mohd Nor

Authors
  1. Perumal Nallagownden
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  2. Elnazeer Ali Hamid Abdalla
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  3. Nursyarizal Mohd Nor
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Corresponding author

Correspondence to Perumal Nallagownden .

Editor information

Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia

    Samsul Ariffin Abdul Karim

  2. Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia

    Mohd Faris Abdullah

  3. Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia

    Ramani Kannan

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Nallagownden, P., Abdalla, E.A.H., Nor, N.M. (2020). Power Consumption Optimization for the Industrial Load Plant Using Improved ANFIS-Based Accelerated PSO Technique. In: Karim, S., Abdullah, M., Kannan, R. (eds) Practical Examples of Energy Optimization Models. SpringerBriefs in Energy. Springer, Singapore. https://doi.org/10.1007/978-981-15-2199-7_3

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  • DOI: https://doi.org/10.1007/978-981-15-2199-7_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2198-0

  • Online ISBN: 978-981-15-2199-7

  • eBook Packages: EnergyEnergy (R0)

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