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Data-Driven Pathways to Sustainable Energy Solutions

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Unified Vision for a Sustainable Future (CEGS 2024)

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Abstract

In the rapidly evolving world of the energy sector, harnessing the power of neural networks and machine learning becomes crucial. This chapter deals with the intricate dimensions of datasets, delineating their types, structures, and classifications that are particularly relevant to energy-related applications. A meticulous exploration of data processing techniques, emphasizing preparation, transformation, labeling, and augmentation, is presented. Additionally, a comparative analysis of optimization algorithms clarifies their role in refining energy-focused models. The complexities, computation times, and accuracies of these optimizers are highlighted. Furthermore, the importance of hyperparameters, their optimal configurations, and the significance of adept tuning are underscored. Serving as a comprehensive guide, this chapter aims to bridge the knowledge gap of stakeholders in the energy domain, providing actionable insights into best practices for data-driven decision-making processes.

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Authors and Affiliations

  1. Energy Systems (Chubu Electric Power) Funded Research Division, Nagoya University, Nagoya, Japan

    Mir Sayed Shah Danish

  2. Research Promotion Unit, Co-Creation Management Department, University of the Ryukyus, Okinawa, Japan

    Mikaeel Ahmadi & Abdul Matin Ibrahimi

  3. School of Business, Istanbul Medipol University, Istanbul, Turkey

    Hasan Dinçer

  4. Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Zahra Shirmohammadi

  5. Cross Laboratories, Cross-Compass Ltd., Tokyo, Japan

    Mahdi Khosravy

  6. Department of Electrical and Electronic Engineering, University of the Ryukyus, Okinawa, Japan

    Tomonobu Senjyu

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  1. Mir Sayed Shah Danish
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  2. Mikaeel Ahmadi
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  3. Abdul Matin Ibrahimi
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  4. Hasan Dinçer
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  5. Zahra Shirmohammadi
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  6. Mahdi Khosravy
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  7. Tomonobu Senjyu
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Correspondence to Mir Sayed Shah Danish .

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Editors and Affiliations

  1. Energy Systems (Chubu Electric Power) Funded Research Division, Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University, Nagoya, Aichi, Japan

    Mir Sayed Shah Danish

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Danish, M.S.S. et al. (2024). Data-Driven Pathways to Sustainable Energy Solutions. In: Danish, M.S.S. (eds) Unified Vision for a Sustainable Future. CEGS 2024. Springer, Cham. https://doi.org/10.1007/978-3-031-53574-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-53574-1_1

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