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Renewable Energy Optimization System Using Fuzzy Logic

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Machine Learning and Metaheuristics: Methods and Analysis

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Contrary to the current decline in the use of fossil fuels, the globe is now paying close attention to the need for energy, global warming, and the ongoing rise in oil prices. Since life cannot exist without energy, the recently developed renewable energy technologies hold out some promise for at least partially alleviating the issue caused by an energy shortage or an imbalance in the distribution of energy between and within nations. In recent decades, hybrid renewable energy systems have gained popularity and are increasingly being used to electrify isolated rural regions throughout the world where grid extension is difficult and uneconomical. These systems combine one or more renewable energy sources, such as solar photovoltaic, wind, microhydro, biomass, and geothermal energy, and they may also include conventional backup generators. This book chapter assembles renewable energy systems along with their benefits and drawbacks, hybrid wind and solar energy systems with various hybrid energy system components to identify the best possible combination of energy components for a typical rural community in order to minimize the total net present cost of the system over its lifetime. In this chapter, a case study using fuzzy logic including a few simulation technique tools is also described, along with some of the component highlights.

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Abbreviations

PV:

Photovoltaic

EV:

Electric Vehicle

REOS:

Renewable Energy Optimization Systems

I/O:

Input

O/P:

Output

AI:

Artificial Intelligence

IoT:

Internet of Things

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Author information

Authors and Affiliations

  1. Vivekananda Institute of Professional Studies—TC, Delhi, India

    Pawan Whig & Ashima Bhatnagar Bhatia

  2. NSUT, Delhi, India

    Bhupesh Bhatia

  3. Department of ECE, BPIT, Delhi, India

    Pavika Sharma

Authors
  1. Pawan Whig
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  2. Bhupesh Bhatia
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  3. Ashima Bhatnagar Bhatia
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  4. Pavika Sharma
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Corresponding author

Correspondence to Pawan Whig .

Editor information

Editors and Affiliations

  1. Muffakham Jah College of Engineering and Technology, Hyderabad, Telangana, India

    Uma N. Dulhare

  2. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Whig, P., Bhatia, B., Bhatia, A.B., Sharma, P. (2023). Renewable Energy Optimization System Using Fuzzy Logic. In: Dulhare, U.N., Houssein, E.H. (eds) Machine Learning and Metaheuristics: Methods and Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-6645-5_8

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